{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis: US Airlines 1988 - 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:43:30.601888Z",
     "start_time": "2019-10-14T20:43:28.081745Z"
    }
   },
   "outputs": [],
   "source": [
    "import vaex\n",
    "from vaex.ui.colormaps import cm_plusmin\n",
    "\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjusting `matplotlib` parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:43:30.623117Z",
     "start_time": "2019-10-14T20:43:30.607918Z"
    }
   },
   "outputs": [],
   "source": [
    "SMALL_SIZE = 12\n",
    "MEDIUM_SIZE = 14\n",
    "BIGGER_SIZE = 16\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtaining the data\n",
    "\n",
    "The original data can be downloaded from [United States Department of Transportation](https://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236 \"U.S. Department of Transportation\"). The selected columns are the same as in this older, better known dataset used for competitions available [here](http://stat-computing.org/dataexpo/2009/the-data.html \"dataexpo 2009\").\n",
    "\n",
    "Please refer to [this](https://nbviewer.jupyter.org/github/vaexio/vaex-examples/blob/master/medium-airline-data-eda/airline-original-data-conversion.ipynb \"airline data conversion notebook\") notebook on how to converd the raw data to the memory-mappable HDF5 format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:43:30.728718Z",
     "start_time": "2019-10-14T20:43:30.635081Z"
    }
   },
   "outputs": [],
   "source": [
    "# read in the data\n",
    "df = vaex.open('./original_data/hdf5/airline_data_1988_2018.hd5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick insights into the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:45:23.138678Z",
     "start_time": "2019-10-14T20:43:30.731048Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>DayOfMonth</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>TailNum</th>\n",
       "      <th>FlightNum</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>CRSDepTime</th>\n",
       "      <th>...</th>\n",
       "      <th>Diverted</th>\n",
       "      <th>CRSElapsedTime</th>\n",
       "      <th>ActualElapsedTime</th>\n",
       "      <th>AirTime</th>\n",
       "      <th>Distance</th>\n",
       "      <th>CarrierDelay</th>\n",
       "      <th>WeatherDelay</th>\n",
       "      <th>NASDelay</th>\n",
       "      <th>SecurityDelay</th>\n",
       "      <th>LateAircraftDelay</th>\n",
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       "  </thead>\n",
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       "      <td>dtype</td>\n",
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       "      <td>int16</td>\n",
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       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
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       "      <td>int32</td>\n",
       "      <td>int32</td>\n",
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       "      <td>183821926</td>\n",
       "      <td>183821926</td>\n",
       "      <td>183821926</td>\n",
       "      <td>183821926</td>\n",
       "      <td>183821926</td>\n",
       "      <td>147156816</td>\n",
       "      <td>183821926</td>\n",
       "      <td>183821926</td>\n",
       "      <td>183821926</td>\n",
       "      <td>183821925</td>\n",
       "      <td>...</td>\n",
       "      <td>183821926</td>\n",
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       "      <td>180136665</td>\n",
       "      <td>144694075</td>\n",
       "      <td>183821926</td>\n",
       "      <td>19923595</td>\n",
       "      <td>19923595</td>\n",
       "      <td>19923595</td>\n",
       "      <td>19923595</td>\n",
       "      <td>19923595</td>\n",
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       "    <tr>\n",
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       "      <td>26027</td>\n",
       "      <td>3685261</td>\n",
       "      <td>39127851</td>\n",
       "      <td>0</td>\n",
       "      <td>163898331</td>\n",
       "      <td>163898331</td>\n",
       "      <td>163898331</td>\n",
       "      <td>163898331</td>\n",
       "      <td>163898331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>2003.6859476273794</td>\n",
       "      <td>6.513381216558464</td>\n",
       "      <td>15.728390529430097</td>\n",
       "      <td>3.9381597057143227</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>1684.4972291118308</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>1332.076048920715</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0023179171781716615</td>\n",
       "      <td>126.51852634644476</td>\n",
       "      <td>124.53201087074639</td>\n",
       "      <td>105.60806368885527</td>\n",
       "      <td>730.8989127553805</td>\n",
       "      <td>16.530673053733526</td>\n",
       "      <td>2.874630406811622</td>\n",
       "      <td>15.328259533482788</td>\n",
       "      <td>0.09065989345798287</td>\n",
       "      <td>21.535320106637382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>8.73569</td>\n",
       "      <td>3.42119</td>\n",
       "      <td>8.78432</td>\n",
       "      <td>1.99011</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>1644.95</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>475.781</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0480889</td>\n",
       "      <td>70.2509</td>\n",
       "      <td>70.2617</td>\n",
       "      <td>68.4631</td>\n",
       "      <td>567.437</td>\n",
       "      <td>44.2425</td>\n",
       "      <td>19.7938</td>\n",
       "      <td>29.8544</td>\n",
       "      <td>2.39507</td>\n",
       "      <td>40.4384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1988</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0</td>\n",
       "      <td>--</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>-162</td>\n",
       "      <td>-719</td>\n",
       "      <td>-2378</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>9912</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>2400</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1865</td>\n",
       "      <td>1440</td>\n",
       "      <td>1399</td>\n",
       "      <td>4983</td>\n",
       "      <td>2580</td>\n",
       "      <td>2692</td>\n",
       "      <td>1848</td>\n",
       "      <td>987</td>\n",
       "      <td>2454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Year              Month          DayOfMonth  \\\n",
       "dtype               int16               int8               int16   \n",
       "count           183821926          183821926           183821926   \n",
       "NA                      0                  0                   0   \n",
       "mean   2003.6859476273794  6.513381216558464  15.728390529430097   \n",
       "std               8.73569            3.42119             8.78432   \n",
       "min                  1988                  1                   1   \n",
       "max                  2018                 12                  31   \n",
       "\n",
       "                DayOfWeek UniqueCarrier    TailNum           FlightNum  \\\n",
       "dtype                int8           str        str               int32   \n",
       "count           183821926     183821926  147156816           183821926   \n",
       "NA                      0             0   36665110                   0   \n",
       "mean   3.9381597057143227            --         --  1684.4972291118308   \n",
       "std               1.99011            --         --             1644.95   \n",
       "min                     1            --         --                   0   \n",
       "max                     7            --         --                9912   \n",
       "\n",
       "          Origin       Dest         CRSDepTime  ...               Diverted  \\\n",
       "dtype        str        str              int16  ...                   int8   \n",
       "count  183821926  183821926          183821925  ...              183821926   \n",
       "NA             0          0                  1  ...                      0   \n",
       "mean          --         --  1332.076048920715  ...  0.0023179171781716615   \n",
       "std           --         --            475.781  ...              0.0480889   \n",
       "min           --         --                  0  ...                      0   \n",
       "max           --         --               2400  ...                      1   \n",
       "\n",
       "           CRSElapsedTime   ActualElapsedTime             AirTime  \\\n",
       "dtype               int32               int32               int32   \n",
       "count           183795899           180136665           144694075   \n",
       "NA                  26027             3685261            39127851   \n",
       "mean   126.51852634644476  124.53201087074639  105.60806368885527   \n",
       "std               70.2509             70.2617             68.4631   \n",
       "min                  -162                -719               -2378   \n",
       "max                  1865                1440                1399   \n",
       "\n",
       "                Distance        CarrierDelay       WeatherDelay  \\\n",
       "dtype              int32               int32              int32   \n",
       "count          183821926            19923595           19923595   \n",
       "NA                     0           163898331          163898331   \n",
       "mean   730.8989127553805  16.530673053733526  2.874630406811622   \n",
       "std              567.437             44.2425            19.7938   \n",
       "min                    0                   0                  0   \n",
       "max                 4983                2580               2692   \n",
       "\n",
       "                 NASDelay        SecurityDelay   LateAircraftDelay  \n",
       "dtype               int32                int32               int32  \n",
       "count            19923595             19923595            19923595  \n",
       "NA              163898331            163898331           163898331  \n",
       "mean   15.328259533482788  0.09065989345798287  21.535320106637382  \n",
       "std               29.8544              2.39507             40.4384  \n",
       "min                   -60                    0                   0  \n",
       "max                  1848                  987                2454  \n",
       "\n",
       "[7 rows x 29 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a high level overview of the DataFrame\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering on the base `df.describe()`\n",
    "\n",
    "This filters out the unphisical measurements of durations and distances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:47:30.278039Z",
     "start_time": "2019-10-14T20:45:23.142385Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>183819525</td>\n",
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       "      <td>183819525</td>\n",
       "      <td>183819525</td>\n",
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       "      <td>183819525</td>\n",
       "      <td>183819525</td>\n",
       "      <td>183819525</td>\n",
       "      <td>183819524</td>\n",
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       "      <td>183819525</td>\n",
       "      <td>19923088</td>\n",
       "      <td>19923088</td>\n",
       "      <td>19923088</td>\n",
       "      <td>19923088</td>\n",
       "      <td>19923088</td>\n",
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       "      <td>NA</td>\n",
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       "      <td>26027</td>\n",
       "      <td>3685117</td>\n",
       "      <td>39127639</td>\n",
       "      <td>0</td>\n",
       "      <td>163896437</td>\n",
       "      <td>163896437</td>\n",
       "      <td>163896437</td>\n",
       "      <td>163896437</td>\n",
       "      <td>163896437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>2003.6859480242917</td>\n",
       "      <td>6.513388210528778</td>\n",
       "      <td>15.728389544037828</td>\n",
       "      <td>3.938158647727982</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>1684.4516931539238</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>1332.0765261583422</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0023172511189983765</td>\n",
       "      <td>126.51923827033315</td>\n",
       "      <td>124.53273411818135</td>\n",
       "      <td>105.61354762491658</td>\n",
       "      <td>730.9031452507561</td>\n",
       "      <td>16.529847431281738</td>\n",
       "      <td>2.8741544483465615</td>\n",
       "      <td>15.32819796810615</td>\n",
       "      <td>0.0906549225702361</td>\n",
       "      <td>21.535796509055224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>8.73574</td>\n",
       "      <td>3.42119</td>\n",
       "      <td>8.78432</td>\n",
       "      <td>1.99011</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>1644.9</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>475.78</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048082</td>\n",
       "      <td>70.2508</td>\n",
       "      <td>70.2613</td>\n",
       "      <td>68.4402</td>\n",
       "      <td>567.439</td>\n",
       "      <td>44.2407</td>\n",
       "      <td>19.7918</td>\n",
       "      <td>29.8528</td>\n",
       "      <td>2.39494</td>\n",
       "      <td>40.4387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1988</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>9912</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>2400</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1865</td>\n",
       "      <td>1440</td>\n",
       "      <td>1399</td>\n",
       "      <td>4983</td>\n",
       "      <td>2580</td>\n",
       "      <td>2692</td>\n",
       "      <td>1848</td>\n",
       "      <td>987</td>\n",
       "      <td>2454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Year              Month          DayOfMonth  \\\n",
       "dtype               int16               int8               int16   \n",
       "count           183819525          183819525           183819525   \n",
       "NA                      0                  0                   0   \n",
       "mean   2003.6859480242917  6.513388210528778  15.728389544037828   \n",
       "std               8.73574            3.42119             8.78432   \n",
       "min                  1988                  1                   1   \n",
       "max                  2018                 12                  31   \n",
       "\n",
       "               DayOfWeek UniqueCarrier    TailNum           FlightNum  \\\n",
       "dtype               int8           str        str               int32   \n",
       "count          183819525     183819525  147154521           183819525   \n",
       "NA                     0             0   36665004                   0   \n",
       "mean   3.938158647727982            --         --  1684.4516931539238   \n",
       "std              1.99011            --         --              1644.9   \n",
       "min                    1            --         --                   0   \n",
       "max                    7            --         --                9912   \n",
       "\n",
       "          Origin       Dest          CRSDepTime  ...               Diverted  \\\n",
       "dtype        str        str               int16  ...                   int8   \n",
       "count  183819525  183819525           183819524  ...              183819525   \n",
       "NA             0          0                   1  ...                      0   \n",
       "mean          --         --  1332.0765261583422  ...  0.0023172511189983765   \n",
       "std           --         --              475.78  ...               0.048082   \n",
       "min           --         --                   0  ...                      0   \n",
       "max           --         --                2400  ...                      1   \n",
       "\n",
       "           CRSElapsedTime   ActualElapsedTime             AirTime  \\\n",
       "dtype               int32               int32               int32   \n",
       "count           183793498           180134408           144691886   \n",
       "NA                  26027             3685117            39127639   \n",
       "mean   126.51923827033315  124.53273411818135  105.61354762491658   \n",
       "std               70.2508             70.2613             68.4402   \n",
       "min                     0                   0                   1   \n",
       "max                  1865                1440                1399   \n",
       "\n",
       "                Distance        CarrierDelay        WeatherDelay  \\\n",
       "dtype              int32               int32               int32   \n",
       "count          183819525            19923088            19923088   \n",
       "NA                     0           163896437           163896437   \n",
       "mean   730.9031452507561  16.529847431281738  2.8741544483465615   \n",
       "std              567.439             44.2407             19.7918   \n",
       "min                    6                   0                   0   \n",
       "max                 4983                2580                2692   \n",
       "\n",
       "                NASDelay       SecurityDelay   LateAircraftDelay  \n",
       "dtype              int32               int32               int32  \n",
       "count           19923088            19923088            19923088  \n",
       "NA             163896437           163896437           163896437  \n",
       "mean   15.32819796810615  0.0906549225702361  21.535796509055224  \n",
       "std              29.8528             2.39494             40.4387  \n",
       "min                  -60                   0                   0  \n",
       "max                 1848                 987                2454  \n",
       "\n",
       "[7 rows x 29 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keep missing values if they fall within the selection criteria.\n",
    "df_filtered = df[((df.ActualElapsedTime>=0).fillmissing(True)) &\n",
    "                 ((df.CRSElapsedTime>=0).fillmissing(True)) &\n",
    "                 ((df.AirTime>0).fillmissing(True)) & \n",
    "                 ((df.Distance > 0).fillmissing(True))]\n",
    "\n",
    "# Describe the filtered dataset\n",
    "df_filtered.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Number of flights per year\n",
    "Let's start by looking at the number of scheduled flights per year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:47:33.334009Z",
     "start_time": "2019-10-14T20:47:30.282974Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "flights_years = df_filtered.Year.value_counts()\n",
    "\n",
    "plt.figure(figsize=(18,4))\n",
    "sns.barplot(x=flights_years.index, y=flights_years.values)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Total number of flights')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dip in flights in 2002 is due to new regulations following the tragedy in 2001. After that, a year or adjustments to the new protocols, number of flights started increasing again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-16T09:44:19.158616Z",
     "start_time": "2019-05-16T09:44:18.405807Z"
    }
   },
   "source": [
    "##### The most busy airports\n",
    "\n",
    "Find the most busy airports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:47:48.402561Z",
     "start_time": "2019-10-14T20:47:33.336228Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "origin_value_counts = df_filtered.Origin.value_counts()\n",
    "dest_value_counts = df_filtered.Origin.value_counts()\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "plt.subplot(121)\n",
    "sns.barplot(x=origin_value_counts.index[:11], y=origin_value_counts.values[:11])\n",
    "plt.title('Most frequent origins')\n",
    "plt.xlabel('Airport code', fontsize=14)\n",
    "plt.ylabel('Number of take-offs')\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.barplot(x=dest_value_counts.index[:11], y=dest_value_counts.values[:11])\n",
    "plt.title('Most frequent destinations')\n",
    "plt.xlabel('Airport code')\n",
    "plt.ylabel('Number of landings')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### When are flights scheduled to depart?\n",
    "\n",
    "Let's look at the number of flights per day of week and month, as well as the number of flights per hour of day and day of week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:47:55.041585Z",
     "start_time": "2019-10-14T20:47:48.406665Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_filtered['Month'] = df_filtered.Month - 1  # to make it start from 0\n",
    "df_filtered['DayOfWeek'] = df_filtered.DayOfWeek - 1  # to make it start from 0\n",
    "\n",
    "# label them as categories\n",
    "df_filtered.categorize(column='Month')\n",
    "df_filtered.categorize(column='DayOfWeek')\n",
    "\n",
    "# Helper lists for labelling the plots \n",
    "label_month_list = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "label_day_list = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:47:55.756934Z",
     "start_time": "2019-10-14T20:47:55.044662Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot number of flights per month vs day of week\n",
    "df_filtered.plot('Month', 'DayOfWeek', colorbar=True, colormap=cm_plusmin, figsize=(15, 5))\n",
    "plt.xticks(np.arange(12), label_month_list)\n",
    "plt.yticks(np.arange(7), label_day_list)\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Day of week')\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:47:59.606107Z",
     "start_time": "2019-10-14T20:47:55.759604Z"
    }
   },
   "outputs": [],
   "source": [
    "# Extract CRS Hour of departure\n",
    "df_filtered['CRSDepHour'] = df_filtered.CRSDepTime // 100 % 24\n",
    "\n",
    "# Treat as a categorical\n",
    "df_filtered.categorize(column='CRSDepHour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:48:02.435248Z",
     "start_time": "2019-10-14T20:47:59.608373Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot number of flights per departure hour vs day of week\n",
    "df_filtered.plot('CRSDepHour', 'DayOfWeek', colorbar=True, colormap=cm_plusmin, figsize=(15, 5))\n",
    "plt.xticks(np.arange(24), np.arange(24))\n",
    "plt.yticks(np.arange(7), label_day_list)\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Overall distributions of flight times, distances, and speed.\n",
    "\n",
    "Now, let's look at the overall distribution of the flight durations, distances and speed of flight. \n",
    "\n",
    "For some of the distributions the limits are adjusted to clip obvious outliers due to likely faulty data recordings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:48:09.125465Z",
     "start_time": "2019-10-14T20:48:02.437717Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "plt.subplot(131)\n",
    "df_filtered.plot1d('AirTime', limits=[0, 500], lw=3, shape=64)\n",
    "plt.xlabel('AirTime [min]')\n",
    "\n",
    "plt.subplot(132)\n",
    "df_filtered.plot1d('Distance', limits='minmax', lw=3, shape=64)\n",
    "plt.xlabel('Distance [miles]')\n",
    "\n",
    "plt.subplot(133)\n",
    "# Calculate the mean speed of the aircraft\n",
    "df_filtered['Speed'] = df_filtered.Distance / (df_filtered.AirTime/60.)  # this is in miles per hour\n",
    "\n",
    "df_filtered.plot1d('Speed', limits=[100, 700], lw=3, shape=64)\n",
    "plt.xlabel('Speed [miles/hour]')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Nearest neighbours\n",
    "\n",
    "Out of curiositly, let's find which are the closest airports which had a regular connection between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:48:09.765664Z",
     "start_time": "2019-10-14T20:48:09.127276Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################]:  100.00% elapsed time  :        0s =  0.0m =  0.0h\n",
      " "
     ]
    },
    {
     "data": {
      "text/plain": [
       "11    2710\n",
       "17      46\n",
       "18       5\n",
       "16       3\n",
       "12       2\n",
       "10       2\n",
       "19       1\n",
       "8        1\n",
       "6        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there are frequent flights at uber-short distances\n",
    "df_filtered[df_filtered.Distance <= 20].Distance.value_counts(progress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok rather large number of flights for distance of 11 miles. Let's find the airports in question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:48:10.323996Z",
     "start_time": "2019-10-14T20:48:09.767523Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SFO    1615\n",
       "OAK    1042\n",
       "JFK      36\n",
       "LGA      17\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see the origins for this short route\n",
    "df_filtered[df_filtered.Distance == 11.].Origin.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like there are quite a large number of flights between SFO and OAK airports. Let's see how that connection evolved throughout the years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:48:14.145166Z",
     "start_time": "2019-10-14T20:48:10.326026Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L6X4QTh84yW1RVS/ozTv0n7rmYmDrJFv2xu1A12Jh3C6ha/o45U7TTTjEjbahun5Gnl9V29FdFfq3tL44ptFf3w502w7dPnz0wD7crKoumma9P6U7Ib4I+FKrnF5KV2E4oyUIZlvuL+mumPXLNq+qf7oJ++RGquoPVXUS3UnyHumeHnAy8M90V2S3Aj7NDH2ebKDr91e6vl1eSddM9jZt3atHXPcvgR0yQodvVfXWqrov3W01u9DdenYZ3VWMnYfMcjFdJW4qztAdGzc65jdwGy4Bth+oPO4ww/RfAPbOjTvu/Wu6/fHjEeP5HN1V7/9OMt0/CX8DPIHuas9UM2wYfbv6v0dh5u/y24Fz6ZqhLwNeNct6hv1eTff7t85n2Qz+fq2zvKr6bFX9JV2l6Vzg32eIRVpIrG+MzvrGhOsbdOfZP7Dub/CkPs8NdSndLTV/QXf70pRf0rXQGdw3bxzDOi8E/nLIZ3IpNz6PTn3PgOuTXyfTJUyfTXd7znSOpWutcpd2jn0No9fx1omDmesoMzmY7vbw+7cY/qJX9oO2nifPsoyrmOZ3LMlOdHWJF9DdDrcV3fm7v52D35+bsm1H0rWeumeLf1+m2YdJHga8jK6l71Z0t3SunW56uP7C16DpfiMuYebv1GCd5rdV9dKqWgE8EXhlkj2mi+XmxoSORtJOth+iO9lOjVtF9+V7VrsS/lyG/2N5UzwmyYNbx1ivB75eVb+ku2K3S5Jnp+sEddMk98sMnbwOxP9LuttA/jFdR1z3omviO/ZH+QEfBl6crsO3rej+MR3VKrrmtjtNjUjy1CRTFbYr6H7k/jjDMg5N8ifpOn99Dt3nBt39z29I6/gxyfIkT5huvc3pdPfnnt6GTxsYnm257wcen+SR7RjZLF0Hxf0K6EjaVYvHJtkyXSd8j6ZLbnyd7urZLdt2XNfK5uqR31vS3eu7Clia5DV091aP4ht0J7V/ald+NkvyoMGJ2rH+gHQdAF5Fl8T5Y6vkvht4U7pOOJek6zjwlnTH4WOTPLzNdxDdifyrY96G/2nzvihdx4RPpuuTZjpvbst+V7tatFmSZ9A1CX9FqxCMFE9VHQV8kC6pM+yq0ZZ023w5XQXqH0bcJuj65tktyZNbwu1FzPwP5JZ0zd3XttYwL5hh2ulM9/v3abrfv79p+/hpdLe9fXLYQpJsm65D7M3ptn8tM/9mSAuG9Y2bxPrGBOobfVX1R7r9/IZW/9iR7h/d92/Icielqi6kSzQ8PslUwuZ9wJPSdfQ/tW8elmQcT156B/AP6VrZkK7z2qmOfE8CntCrlxzBjRMSJ9D1dfdYZt6nW9Jd2LmqfRcPmGHaQR8GXtK+J7dltO/JLdp+mnotbTFcDVzRlvOaqYnbcfIKupbjz0uyVTq70N1COuW7dHWz2yS5A73fOboWRUX3HUm6zs9na117U7ZtS7o65Ookd6K7bW2maa+jS2huSnc7++azxDLMoeke3nFPYB9u+I04EXhNkm2SLKfrJmDazz/J45PsnCR0x8EfsV5zPRM6uilex42/zM+n+wG7nO6f62H/LN4UH6S7Ovcbuk5GnwnQrtY8Ang6XXb3UrpM8y1vwrKfQXeF/mK6Zp6HVdXnNzDeYf6drvXA2cB36P4Zu44Rfnja1Yo3AF9J19zygXQdwX09yVq6fkNeXK23/GmcTtdPyX/TdXb2uTb+LW3+zyVZQ9ex4ANmWO/UsrbkhqdSDA7Pttxf0rWQeBXdCeqXdMfL+vz2/LYt5xd09yQfRddp4hnt+HgR3YntCrqWGR9fj3Wsj88C/0XXx80FdMmWmZrjX69VAB5Pd6/xL+iudD1tyKTL6I6rK9o6LqdrjQTdCfl7wDfpvjdH0t0//yO6joX/le6E/Hjg8QNNWMexDb+nuyK1b4vvaXT3OU83/eV0zeA3o7uidTld5fjZVTV1oh85nqp6PV1/SV9I72knzQlt/ovaur42yja15V4GPJWume/ldJ1FfmWGWV5Od9ytofusZns6zDDT/f5dDjyOLil3Od1Vwse1GIfZpE17cVvWHnT9NkiLhfWN0VjfmEx9Y8pU8uHv6P4R/hldnyQfpLuYMmeS7JPkrFGmraoL6JI6f5PkiKr6OV1nvofS7Ztf0J0jxvF/4JuAz9BdWFlD9728X4vjbODFdHWzi+i+S5cOzP8lutuRvt6SUdM5iC4hsIautc5NOce+ne4YnaorfWSEeT5Ld5vm1OsQum29Nd1v0Ffp6inXq6oP0H3396Grz10G/AddB+lT9aLj6W4VvIBuv/1Hb/6z6foFnLrYtyvdRctxbdthdBfcVtN9j06eYdpP07Wo/gnd7eq/bTHdVGfQfW8+B/xjdV0IALyW7qEg36P7/fo6Xavr6dyV7ha3tXR1sbdU1RnrEc9GaaoHdkkT0lqLvKOqBm+ZkCSgexQyXUeoh8x3LJIWJ+sb49FaVX2p3fKiCUv3COp3V9Xx8x2LxiPdrZo/qapJdX2gHlvoSGPWmhY+pt0asT1dRvyj8x2XJEnaeFjfGL90/bv9NXDmfMdyc9BaaN2D7vYsSevBhI40fqFrSngFXRPoH9K7z1aSJGkMrG+M3y/onrj00vkOZGOX5AN0tx29uLqnM0paD95yJUmSJEmStMjYQkeSJGk9JbllkncluSDJmiTfaX2ZkGRFkkqytvc6dL5jliRJG4el8x2AJEnSIraU7ok6e9DdrvEY4MPtMa1Ttqqq6+YjOEmStPG6Wd9ytc0229SKFSvmOwxJkjTPvvWtb11WVcvHsawkZ9P1bfIt4Hxg05ua0LGOIkmSYOY6ys26hc6KFSs480w7sZck6eYuyQVjWs62wC7AOb3RFyQp4PPAK6rqsmnm3R/YH2CHHXawjiJJkmaso9iHjiRJ0hgk2RT4APDeqjoXuAy4H7AjcF9gy1Y+VFUdV1Urq2rl8uVjaSwkSZI2YjfrFjqSJEnjkGQT4H3A74EDAapqLTDVzOZXSQ4ELkmyrKp+Oz+RSpKkjYUJHUmSpA2QJMC7gG2Bx1TVH6aZdKrjwsxJYJIkaaNmQkeSJGnDvB24G7BXVV0zNTLJA4ArgZ8AtwHeCpxWVavnJUpJkrRRsQ8dSZKk9ZRkR+AA4N7ApUnWttczgZ2AzwBrgO8D1wLPmLdgJUnSRsUWOpIkSeupqi5g5luoTpyrWCRJ0s2LLXQkSZIkSZIWGRM6kiRJkiRJi4y3XPWsevv75zuE6y1/wbPmOwRJkrRAzGcdxTqJJEkLky10JEmSJEmSFpk5TegkOTDJmUmuTXL8NNMclqSS7NUblyRHJrm8vY5Kkl75iiSnJrk6ybn9eSVJkiRJkjY2c91C52LgCODdwwqT7Aw8BbhkoGh/4InA7sC9gMfRPSJ0yonAd4DbAq8GPpJk+VgjlyRJkiRJWiDmNKFTVadU1ceAy6eZ5BjglcDvB8bvAxxdVRdW1UXA0cC+AEl2Ae4DHFZV11TVycD3gL0nsAmSJEmSJEnzbsH0oZPkqcDvq+rTQ4p3A87qDZ/Vxk2V/ayq1kxTPrie/dttX2euWrVqDJFLkiRJkiTNrQWR0EmyBfAPwEummWQLYHVveDWwRetHZ7BsqnzLYQuqquOqamVVrVy+3LuyJEmSJEnS4rMgEjrAa4H3VdX505SvBZb1hpcBa6uqhpRNla9BkiRJkiRpI7RQEjoPB16U5NIklwJ3Aj6c5JWt/By6DpGn7N7GTZXtlGTLacolSZIkSZI2KkvncmVJlrZ1LgGWJNkMuI4uobNpb9JvAi8D/qsNnwC8LMmngQIOAv4VoKp+nOS7wGFJDgEeTfckLDtFliRJkiRJG6U5TegAhwCH9YafBby2qg7vT5Tkj8AVVbW2jToW2Inu6VUA72zjpjwdOB64AvgF8JSqssdjSZIkSZK0UZrThE5L3Bw+wnQrBoYLOLi9hk3/c2DPDQxPkiRJkiRpUVgofehIkiRJkiRpRCZ0JEmSJEmSFhkTOpIkSZIkSYuMCR1JkiRJkqRFxoSOJEmSJEnSImNCR5IkSZIkaZExoSNJkiRJkrTImNCRJEmSJElaZEzoSJIkSZIkLTImdCRJkiRJkhYZEzqSJEmSJEmLjAkdSZIkSZKkRcaEjiRJkiRJ0iJjQkeSJEmSJGmRMaEjSZIkSZK0yJjQkSRJkiRJWmRM6EiSJEmSJC0yc5rQSXJgkjOTXJvk+N74Byb5fJLfJFmV5KQkd+iVJ8mRSS5vr6OSpFe+IsmpSa5Ocm6SveZyuyRJ0s1TklsmeVeSC5KsSfKdJI/ulT+81U2ubnWVHeczXkmStPGY6xY6FwNHAO8eGH8b4DhgBbAjsAZ4T698f+CJwO7AvYDHAQf0yk8EvgPcFng18JEky8cfviRJ0jqWAr8E9gBuDRwKfLhdbNoGOKWN2xo4E/jQfAUqSZI2LkvncmVVdQpAkpXAHXvj/6s/XZJjgNN7o/YBjq6qC1v50cDzgXck2QW4D/CIqroGODnJS4C9gXdMcHMkSdLNXFVdBRzeG/XJJOcD96W70HROVZ0EkORw4LIku1bVuXMdqyRJ2rgs1D50Hgqc0xveDTirN3xWGzdV9rOqWjNNuSRJ0pxIsi2wC109Zp36S0v+nId1FEmSNAYLLqGT5F7Aa4BX9EZvAazuDa8Gtmj96AyWTZVvOc3y92/9+Jy5atWq8QUuSZJu1pJsCnwAeG9rgWMdRZIkTcxICZ0keyR5QG943yRnJDk2yRbjCibJXYD/Al5cVV/uFa0FlvWGlwFrq6qGlE2Vr2GIqjquqlZW1crly+1mR5IkbbgkmwDvA34PHNhGW0eRJEkTM2oLnX8Bbg+Q5K7AscDZwJ8BbxxHIO2pD18AXl9V7xsoPoeuQ+Qpu3PDLVnnADsl2XKackmSpIlpLYbfBWwL7F1Vf2hF69RfkmwO7Ix1FEmSNAajJnR2Br7X3u8NfL6qXkjXMfHjR11ZkqVJNgOWAEuSbNbGbQ98EXhbVQ3ryPgE4GVJtk+yHXAQcDxAVf0Y+C5wWFvek+iehHXyqHFJkiRtgLcDdwMe3x7QMOWjwD2S7N3qP68BzrZDZEmSNA6jPuWq6JIwAA+nq6AAXEr3BIdRHQIc1ht+FvDatvyd6JIy15dX1dTtXMe28qmk0jvbuClPp0vwXAH8AnhKVXnzuSRJmqjWwvgA4Frg0q6xDgAHVNUHkuwNHAO8H/g6XZ1FkiRpg42a0PkmcGiSzwMPAfZv41fQJXVGUlWHs+6jPfteO8N8BRzcXsPKfw7sOWockiRJ41BVFwCZofwLwK5zF5EkSbq5GPWWq5cC96a7wvSGqjqvjX8q8NVJBCZJkiRJkqThRmqhU1Xfo+uXZtDLgevGGpEkSZIkSZJmNOpjy7+YZKshRbcAPjfekCRJkiRJkjSTUW+52pMueTNoM7o+dSRJkiRJkjRHZrzlKsl9eoP3SvKb3vAS4JHARZMITJIkSZIkScPN1ofOmXSPFC+G31p1DfB34w5KkiRJkiRJ05stoXNnukdx/gy4P7CqV/Z74NdV9ccJxSZJkiRJkqQhZkzoVNUF7e2ofe1IkiRJkiRpwkZ6bDlAkjvRdYB8OwYSPFX1pjHHJUmSJEmSpGmMlNBJ8kzg3cB1dLddVa+4ABM6kiRJkiRJc2TUFjqvA44GDrXPHEmSJEmSpPk1at842wLvNJkjSZIkSZI0/0ZN6HwaeMAkA5EkSZIkSdJopr3lKsmTe4OfB45MshvwPeAP/Wmr6pTJhCdJkiRJkqRBM/Wh85Eh4141ZFwBS8YTjiRJkiRJkmYzbUKnqka9HUuSJEmSJElzyKSNJEmSJEnSIjPSY8uTvGaaogJ+B/wU+ExVXTOuwCRJkiRJkjTcSAkd4KnADsDmwMVt3HbAVcAq4E7Ar5PsUVU/m24hSQ4E9gXuCZxYVfv2yh4OvK2t5+vAvlV1QSsL8E/Afm3ydwGvrKpq5SuA99A9iesXwIFV9YURt02SJEmSJGlRGfWWq6OBbwIrqmqHqtoBWEGXeHkdXXLnx8CbZ1nOxcARwLv7I5NsA5wCHApsDZwJfKg3yf7AE4HdgXsBjwMO6JWfCHwHuC3wauAjSZaPuG2SJEmSJEmLyqgJncOAl1XVhVMj2vuDgddV1f0WyywAACAASURBVOV0iZQHzrSQqjqlqj4GXD5Q9GTgnKo6qap+BxwO7J5k11a+D3B0VV1YVRfRJZj2BUiyC3Af4LCquqaqTqZ7tPreI26bJEmSJEnSojJqQmdbYLMh428J3K69/xXwJ+sZx27AWVMDVXUVcF4bf6Py9r5f9rOqWjNN+TqS7J/kzCRnrlq1aj3DlSRJkiRJmj+jJnS+AByb5H5JNmmv+wFvBz7fprkncP56xrEFsHpg3Gpgy2nKVwNbtL51Zpt3HVV1XFWtrKqVy5d7V5YkSZIkSVp8Rk3o7EfXAufrwLXt9bU27vltmjXAy9czjrXAsoFxy9oyh5UvA9a2TpFnm1eSJEmSJGmjMtJTrqrq18CjktwVuCsQ4IdV9ePeNKduQBzn0PWTA0CSzYGd2/ip8t2Bb7Th3QfKdkqyZe+2q92BD25APJIkSZIkSQvWqC10AKiqH1XVx6vqP/vJnFElWZpkM2AJsCTJZkmWAh8F7pFk71b+GuDsqjq3zXoC8LIk2yfZDjgIOL7F9GPgu8BhbXlPonsS1sk3NT5JkiRJkqTFYNoWOkneCvx/VXVVez+tqnrRiOs7hO6JWVOeBby2qg5PsjdwDPB+ulu7nt6b7lhgJ7qnVwG8s42b8nS6BM8VwC+Ap1SVPR5LkiRJkqSN0ky3XN0T2LT3fjo16sqq6nC6R5IPK/sCsOs0ZUX3iPSDpyn/ObDnqHFIkiRJkiQtZtMmdKrqYcPeS5IkSZIkaX7dpD50JEmSJEmSNP9m60NnJDehDx1JkiRJkiRtoNn60BnFyH3oSJIkbWySHAjsS1d3OrGq9m3jVwDnA1f1Jj+yql4/txFKkqSN0UwJnX2AC6vqf+cqGEmSpEXoYuAI4JHArYaUb1VV181tSJIkaWM3Ux865wO3BUjyxSRbzU1IkiRJi0dVnVJVHwMun+9YJEnSzcdMCZ01wPL2fk9ueIS5JEmSRndBkguTvCfJNvMdjCRJ2jjMdMvVF4AvJvlhG/5okt8Pm7Cq/mLskUmSJC1ulwH3A75L1+r5bcAH6G7NupEk+wP7A+ywww5zFKIkSVqsZkroPBt4LnAXYA/gR8DVcxGUJEnSYldVa4Ez2+CvWufJlyRZVlW/HTL9ccBxACtXrvShE5IkaUbTJnSq6hq6K0kkuTdwUFVdOVeBSZIkbWSmkjSZ1ygkSdJGYaYWOterqodNOhBJkqTFKMlSujrVEmBJks2A64D7AlcCPwFuA7wVOK2qVs9XrJIkaeMxU6fIkiRJmt0hwDXA3wPPau8PAXYCPkP3oInvA9cCz5inGCVJ0kZmpBY6kiRJGq6qDgcOn6b4xLmLRJIk3ZzYQkeSJEmSJGmRmTahk+TdSbZs7x/a7g+XJEmSJEnSPJuphc6zgM3b+1OBrScfjiRJkiRJkmYzU6ubnwN/l+RzdI/X/LMkVwybsKq+NIHYJEmSJEmSNMRMLXReATyfrnVOAR8FThvyOnVcwSRZkeTTSa5IcmmSY6Zu9Ury8CTnJrk6yalJduzNlyRHJrm8vY5KknHFJUmSJEmStJBMm9Cpqv+sqtvR3WoVYDdg+ZDX7cYYz78BvwbuANwb2AN4YZJtgFOAQ1s8ZwIf6s23P/BEYHfgXsDjgAPGGJckSZIkSdKCMWtHx1V1ZZKHAT+pqusmHM+dgWOq6nfApUk+Q5dIejJwTlWdBJDkcOCyJLtW1bnAPsDRVXVhKz+arnXROyYcryRJkiRJ0pwb6clVVXV6klsm+b/A3eluwfoB8MGqunaM8bwFeHqS04DbAI+ma5WzJ3BWL56rkpxHl+w5t/09q7ecs9o4SZIkSZKkjc5MfehcL8ndgR8DbwIeADwQeDPw4yR3G2M8p9MlYn4LXEh3a9XHgC2A1QPTrga2bO8Hy1cDWwzrRyfJ/knOTHLmqlWrxhi6JEmSJEnS3BgpoUPXcua7wA5V9ZCqegiwA11LmH8ZRyBJNgE+S9dXzubANnStdI4E1gLLBmZZBqxp7wfLlwFrq6oG11NVx1XVyqpauXz58nGELkmSJEmSNKdGTeg8CHhVVf12akR7/2rgwWOKZWvgTnR96FxbVZcD7wEeA5xD1+ExAEk2B3Zu4xksb+/PQZIkSZIkaSM0akLnd8BWQ8bfupVtsKq6DDgfeEGSpUm2ouvs+Cy6R6bfI8neSTYDXgOc3TpEBjgBeFmS7ZNsBxwEHD+OuCRJkiRJkhaaURM6nwD+PcmDkixprwcDxwIfH2M8TwYeBawCfgpcB7y0qlYBewNvAK6g68fn6b35jm0xfg/4PvCpNk6SJEmSJGmjM9JTroAXA+8Fvgz8sY3bhC6Z85JxBVNV36V7otWwsi8Au05TVsDB7SVJkiRJkrRRG/Wx5VcCT0hyF+BuQIAfVNVPJxmcJEmSJEmSbmzUFjoAtASOSRxJkiRJkqR5NGofOpIkSZIkSVogTOhIkiRJkiQtMiZ0JEmSJEmSFplZEzpJliZ5YZLt5iIgSZIkSZIkzWzWhE5VXQe8Edh08uFIkiRJkiRpNqPecvU14D6TDESSJEmSJEmjGfWx5f8OHJ1kR+BbwFX9wqr69rgDkyRJkiRJ0nCjJnQ+2P6+aUhZAUvGE44kSZIkSZJmM2pC584TjUKSJEmSJEkjGymhU1UXTDoQSZIkSZIkjWbUTpFJ8ugkn0zygyR3auP2S/LwyYUnSZIkSZKkQSMldJI8E/gw8BO626+mHmG+BDh4MqFJkiRJkiRpmFFb6BwMPL+qXgpc1xv/NeDeY49KkiRJkiRJ0xo1ofOnwP8MGb8WWDa+cCRJkiRJkjSbURM6FwO7DBn/UOC88YUjSZIkSZKk2Yya0DkOeGuSB7XhOyXZBzgKePtEIpMkSZIkSdJQIyV0quoo4BTg88DmwKnAO4B3VNXbxhlQkqcn+WGSq5Kcl+QhbfzDk5yb5OokpybZsTdPkhyZ5PL2OipJxhmXJEnSMEkOTHJmkmuTHD9QNm39RZIkaUOM/Njyqno1sA1wf+CBwPKqOnScwST5S+BI4DnAlnS3dP0syTZ0CaVDga2BM4EP9WbdH3gisDtwL+BxwAHjjE2SJGkaFwNHAO/ujxyh/iJJkrTeRk7oNAX8Drga+OP4w+G1wOuq6mtV9b9VdVFVXQQ8GTinqk6qqt8BhwO7J9m1zbcPcHRVXdimPxrYdwLxSZIkraOqTqmqjwGXDxTNVn+RJElabyMldJLcMsm/AL8BzgLOBn6T5C1JNhtHIEmWACuB5Ul+muTCJMckuRWwW1svAFV1FV1nzLu1UeuUt/e7MUSS/Vuz6DNXrVo1jtAlSZKGma3+sg7rKJIk6aYYtYXO24GnAPvRPcL8Lu39k4B/G1Ms2wKbtvU8BLg38H+AQ4AtgNUD06+muy2LIeWrgS2G9aNTVcdV1cqqWrl8+fIxhS5JknQjs9Vf1mEdRZIk3RRLR5zuqcCTq+rzvXE/S/Jr4GTguWOI5Zr291+r6hKAJG+iS+h8CVg2MP0yYE17v3agfBmwtqpqDHFJkiStj8H6Caxbf5EkSVpvo7bQuQq4aMj4i7ghEbNBquoK4EK6fnoGnUPX4TEASTYHdm7jb1Te3p+DJEnS/Jmt/iJJkrTeRk3o/CtwWOvPBoD2/tBWNi7vAf4uye2S3AZ4CfBJ4KPAPZLs3frseQ1wdlWd2+Y7AXhZku2TbAccBBw/xrgkSZKGSrK01U+WAEuSbJZkKbPXXyRJktbbtLdcJfn4wKg9gYuSnN2G79nm33yM8bye7tHoP6Z7mtaHgTdU1e+S7A0cA7wf+Drw9N58xwI7Ad9rw+9s4yRJkibtEOCw3vCzgNdW1eGz1F8kSZLW20x96Aw+evPkgeHzxxwLVfUH4IXtNVj2BWDoYz5bXzkHt5ckSdKcqarD6R5JPqxs2vqLJEnShpg2oVNVz5nLQCRJkiRJkjSaUfvQkSRJkiRJ0gIx0mPLWwfFhwMPA27HQCKoqm439sgkSZIkSZI01EgJHbqnSO0GvBf4FcMfLS5JkiRJkqQ5MGpCZ09gj6r69gRjkSRJkiRJ0ghG7UPnvJswrSRJkiRJkiZo1CTNi4F/TLJ7kiWTDEiSJEmSJEkzG/WWq58CtwK+DZBkncKqMskjSZIkSZI0R0ZN6JwI3Bp4EXaKLEmSJEmSNK9GTeisBO5fVd+fZDCSJEmSJEma3ah96PwAWDbJQCRJkiRJkjSaURM6hwBvSrJXkm2TbN1/TTJASZIkSZIkrWvUW64+3f5+jnX7z0kbtlNkSZIkSZKkOTJqQudhE41CkiRJkiRJIxspoVNVp086EEmSJEmSJI1mpIROkvvMVF5V3x5POJIkSZIkSZrNqLdcnUnXV0564/p96diHjiRJkiRJ0hwZ9SlXdwZ2an/vDOwCPB34HvC4cQaU5E+T/C7J+3vjHp7k3CRXJzk1yY69siQ5Msnl7XVUkgxfuiRJkiRJ0uI3ah86FwwZ/dMkq4HDgP8aY0xvA745NZBkG+AUYD/gE8DrgQ8BD2yT7A88EdidrtXQ54GfAe8YY0ySJEka4sJjnjuv67/jge+e1/VLkjRfRm2hM53zgXuPIxCAJE8HrgT+uzf6ycA5VXVSVf0OOBzYPcmurXwf4OiqurCqLgKOBvYdV0ySJEmSJEkLzaidIm89OAq4A11y5UfjCCTJMuB1wMOB5/WKdgPOmhqoqquSnNfGnztY3t7vNo6YJEmSJEmSFqJRO0W+jHU7QYYuqfNL4GljiuX1wLuq6pcDXeBsAawamHY1sGWvfPVA2RZJUlWDMZNkf7rbtNhhhx3GFLokSZIkSdLcGTWh87CB4f+lS7L8tKqu29Agktwb2Av4P0OK1wLLBsYtA9ZMU74MWDssmQNQVccBxwGsXLly6DSSJEmSJEkL2aidIp8+4Tj2BFYAv2itc7YAliS5O13nxvtMTZhkc2Bn4Jw26hy6DpG/0YZ375VJkiRJkiRtdGZM6AzpO2eoqvrNBsZxHPAfveGX0yV4XtCG35hkb+BTwGuAs6vq3FZ2AvCyJJ+muy3sIOBfNzAeSZIkSZKkBWu2FjrD+s4ZVCMsZ+YFVF0NXD01nGQt8LuqWtWG9waOAd4PfB14em/2Y4GdgO+14Xe2cZIkSZIkSRul2RIxg33n9D0KeDGwwX3oDKqqwweGvwDsOs20BRzcXtIGOfZ9j5zvEK53wLM/O98hSJIkSVqETnv/4HOF5taez1o+r+u/uZgxoTOs75wk9wGOBB5K1xLm9ZMJTZIkSZIkScNsMuqESe6c5IN0tzz9Brh7Vb1o6rYoSZIkSZIkzY1ZEzpJbpvkLcC5wO2BP6uqp1XVeROPTpIkSZIkSTcyY0InyauA84A9gCdU1V9U1ZlzEpkkSZIkSZKGmq1T5COAa4ALgRcmeeGwiarqr8YdmCRJ0sYgyWnAA7nhQRIXVdVd5y8iSZK0MZgtoXMCsz+2XJIkSTM7sKreOd9BSJKkjcdsT7nad47ikLQenvPRR813CAC850mfme8QJEmSJOlmZeSnXEmSJGm9/WOSy5J8Jcme8x2MJEla/EzoSJIkTdYrgZ2A7YHjgE8k2XlwoiT7JzkzyZmrVq2a6xglSdIiY0JHkiRpgqrq61W1pqqurar3Al8BHjNkuuOqamVVrVy+fPncBypJkhYVEzqSJElzq4DMdxCSJGlxm+0pV9IG++y7bnQRct488nmfnu8QJEk3I0m2Ah4AnE732PKnAQ8FXjKfcUmSpMXPhI4kSdLkbAocAewK/BE4F3hiVf1oXqOSJEmLngmdReoXb33KfIdwvR1e9JH5DkGSpAWpqlYB95vvOCRJ0sbHPnQkSZIkSZIWGRM6kiRJkiRJi4wJHUmSJEmSpEXGhI4kSZIkSdIis2ASOklumeRdSS5IsibJd5I8ulf+8CTnJrk6yalJduyVJcmRSS5vr6OSZH62RJIkSZIkabIWTEKH7olbvwT2AG4NHAp8OMmKJNsAp7RxWwNnAh/qzbs/8ERgd+BewOOAA+YudEmSJEmSpLmzYB5bXlVXAYf3Rn0yyfnAfYHbAudU1UkASQ4HLkuya1WdC+wDHF1VF7byo4HnA++Yuy2QJEmSJEmaGwuphc46kmwL7AKcA+wGnDVV1pI/57XxDJa397sxRJL9k5yZ5MxVq1ZNInRJkiRJkqSJWpAJnSSbAh8A3tta4GwBrB6YbDWwZXs/WL4a2GJYPzpVdVxVrayqlcuXLx9/8JIkSZIkSRO24BI6STYB3gf8HjiwjV4LLBuYdBmwZpryZcDaqqoJhipJkiRJkjQvFkwfOtA9rQp4F7At8Jiq+kMrOoeun5yp6TYHdm7jp8p3B77RhnfvlUmSJEmSNDZv+uil87r+lz3p9vO6fi0MC62FztuBuwGPr6preuM/Ctwjyd5JNgNeA5zdbscCOAF4WZLtk2wHHAQcP4dxS5IkSZIkzZkFk9BJsiPdo8bvDVyaZG17PbOqVgF7A28ArgAeADy9N/uxwCeA7wHfBz7VxkmSJEmSJG10FswtV1V1AXCjTox75V8Adp2mrICD20uSJEmSJGmjtmBa6EiSJEmSJGk0JnQkSZIkSZIWGRM6kiRJkiRJi4wJHUmSJEmSpEVmwXSKLEmSJI3bqe987Lyu/2H7fWpe1y8tVnuf/I15Xf/Je99/Xte/sbvkqEvmbd13OPgOM5b/6q1nzFEkw237ogePPK0tdCRJkiRJkhYZEzqSJEmSJEmLjAkdSZIkSZKkRcaEjiRJkiRJ0iJjQkeSJEmSJGmRMaEjSZIkSZK0yJjQkSRJkiRJWmRM6EiSJEmSJC0yS+c7AEmSJOnm6vj3PmJe17/vPp+bsfzVJz1qjiIZ7g1P/cyM5Y/52KvmKJLhPv3Ef5ix/LEn//scRXJjn9r7+TOWP/4jH52jSIb7xFOeNK/rlzYGttCRJEmSJElaZEzoSJIkSZIkLTImdCRJkiRJkhaZjSahk2TrJB9NclWSC5L8zXzHJEmSZB1FkiRNwsbUKfLbgN8D2wL3Bj6V5KyqOmd+w5IkSTdz1lEkSdLYbRQtdJJsDuwNHFpVa6vqDODjwLPnNzJJknRzZh1FkiRNykaR0AF2Af5YVT/ujTsL2G2e4pEkSQLrKJIkaUJSVfMdwwZL8hDgpKq6fW/c84FnVtWeA9PuD+zfBu8K/GjM4WwDXDbmZU7KYooVFle8xjoZxjoZxjo5iynem3usO1bV8jEvcyHVURby57uQYwPj21DGt/4WcmxgfBvK+DbMQo5v3LFNW0fZWPrQWQssGxi3DFgzOGFVHQccN6lAkpxZVSsntfxxWkyxwuKK11gnw1gnw1gnZzHFa6wTsyDqKAt5ny3k2MD4NpTxrb+FHBsY34Yyvg2zkOOby9g2lluufgwsTfKnvXG7A3Y2KEmS5pN1FEmSNBEbRUKnqq4CTgFel2TzJA8CngC8b34jkyRJN2fWUSRJ0qRsFAmd5oXArYBfAycCL5inx4FO7HauCVhMscLiitdYJ8NYJ8NYJ2cxxWusk7MQ6igLeZ8t5NjA+DaU8a2/hRwbGN+GMr4Ns5Djm7PYNopOkSVJkiRJkm5ONqYWOpIkSZIkSTcLJnQkSZIkSZIWGRM600hyYJIzk1yb5PiBsv2S/DTJ2iSfSbJdr+yWSd6R5FdJfpPkE0m275XfO8mXk6xOcmGS1yzgWP88yTeSrElydpIHb2isGxjvVknem+TX7XX4wLwrkpya5Ook5ybZawHH+vok30ty3WDZQoo1ye2SnJjk4nbMfiXJAxZirK381CSrkvw2yVlJnrBQY+1Nt0eSSnLEQo01yc+TXNPmXZvkcwsx1iQ79GKcelWSgxZivK18rOeEdL/r70pyQbrf7u8keXSv/OHpfh+vbt+XHXtlSXJkksvb66gk6ZWP9fs1qVgneRwsBBtwLE68fjLB2MZSH5ng93gs341JxdebboPON5OML8mLk5yf5KokP0yyy0KIb1y/J5nsb/MG138nGV9vuvU+/ia8/za4jjPp/bch349JxZbF8d0Yx3ltkvGN53/tqvI15AU8GXgi8Hbg+N74Peg6NdwNuEUrP71XfjBwFrAtsBndUyxO6ZX/AHgDsATYGbgE+KuFFiuwNXAZ8NQW67OAK4DbzOO+fQ9wEvAnwArgPOA5vfL/Ad5E1/Hk3sCVwPIFGus+wKOB/wQOn+djdtpYgZ2AlwF3aMfB/u242GKhxdrK7wUsbe8fAKwB7rAQY23TbAp8F/gacMRCPAZa+c+BvcZxnM7Ffu1Ne2fgj8CKhRovYz4nAJsDh7d1bQI8rn0PVgDbAKvpftc3A94IfK037wHAj4A7Atu32P52Ut+vScY6qeNgIbw24FiceP1kErExxvrIBsQ3J+eeScXXptng880E999+wNnA3YG042/rhRLfwDrW6/eEyf42b3D9d5LxjeP4m/D++zkbWMeZcHwb9P2Y9Ge7wL8b4zivTSQ+xnlu25CD9+bwAo5g3ZPCPwNv6w1vBxSwcxt+O3BUr/yxwI96w1cDd+8NnwT8fwst1nawnjOw/B8Dz5vHfXsZcL9e+auAL7f3uwDXAlv2yr/MND868xnrwHLfz5gSOpOOtVf+W+C+Cz1W4P7A74D7L9RYgb8HjgKOZwwJnUnFygQSOnN0DBwGnLqQ42WC54TeMs+mq+TvD3y1N35z4Bpg1zb8VWD/Xvnz6FVM/v/27jVWrqqMw/jz2qKl0ooUARGRgARJi6hcvCAfEBFjVEJADFQu+kGJQYMfVGKiAkaIoIGIBryggFxKIiqBUKsQIQpRIVyijYjGgC2mcpOmLRWKvH5Y+5BhPFp7Zq3pnvb5JTsnZ++ZPf8zs25nzb4M7bNq/WqctXo56MMyg7I4tvFJzWw0GI80bndGrhst8lGxv6mZj/IP0grg8M1YNzZLv0KF9o6G498a+VqUv9r5aDTGqfT5Vq8ftT/bgW29qhvd703GWJU+22p9m6dcbbrolsHfARZ1Py8FDomIXSNiLrAYWDrw+AuBkyJim4jYB3grcHMPsw4/d2rdItrZWF6m2T61bSHwl8xcM7D9vm59C6NkHbdqWSPiDZRvt/5cM+DQa4+UNSJujIh/Ar8BbgXuqh/z+deecdbukMyPAGc3yjeoRhm4KsopBT+LiP0bZBx87Vp16yTg8nrRpjVq3qZ9QkTsTBnwL6e0h/dNbcvMdZRvoqfayRdsZ5o2tGX9qp11wDjKQR/0eXzS9/FI3/uevvc3o+TbrVsWRcSK7rSSsyKi5v8ovetXKrZ3Tca/NdvjFuWvQX9RdYxTMV/1+tH3vrZyvur9WsV81fo2J3Q23U3AcRHx+ojYFvgCZZZ/brf9AeCvwMOUIxn25YUN2I3AsZTZu/uBSzPzzh5mvQPYNSKO7yrByZRD1ebSzsby/hQ4IyLmRcRrKZ3D1LbtKIe8DVoNzOth1nGrkjUi5lMOgz8rM4ff695kzcz3Uj739wDLMvO5nmb9OvD5zFzbKF/NrIsph5a+BvgFsCwitu9pVgAi4lDK6Rs/bJSzVt5mfUJEbANcBVyemfez8XZyePtqYLvB871b1a8WWbv9jqsc9EGfxyd9H4/0ve/pe38zSr7dup/vAvYDDgOOp3yT3Yd8z6vVnlRu76qPfxu0x1XLX4N8Vcc4lfNVrR9972sb5Kvar1XOV61vc0JnE2XmLZRDyq4DHqIcprcGWNk95GLKOXQLKIdd/YjuW6aI2IHSaZzdPebVwJER8fG+Zc3Mx4GjKNdP+TvwbsqM5koa+T/yfpJSIf9EufbMNQPb1gLzh3Y5v3t+37KOVY2s3QDoBsphguf2OWu3nw2ZuZRSv97ft6wR8T7K4dHXtshWM2v3/Nszc31mPtV9/k8Ch/Yx64CTgetaT5iNWA6a9Qndt3c/AJ4BTutWb6ydHN4+H1ib3XHAU2rXr5ZZGVM56IM+j0/6Ph7pe9/T9/5mxPdvfffzvMx8MjMfBL5FmRjrQ75BI7cnDdq7quPf2vlql78W/UXNMU6DfNXqR9/72gZlr2q/Vjtfzb7NCZ0ZyMxvZubembkTpXOYDfy+27w/5bzdJzLzaeAi4OCI2JFygdl/ZeYVmflsZq4EllCx06qYlcy8LTMPyswdgBOBfYDftsq6sbxdzsWZuUtmLqSU36k8y4E9I2LwG4n9u/V9yzp2o2SNiJcAP6F8c/qxPmedxmzKbHffsh4OHBgRqyJiFfBB4PSIuL6HWafdHf95mGhvsnYTkB9gTKfZjJC3SZ/QffNzKeXbsmMyc0O3aTmlXZx63Esp9WP5dNvZeBs6cv1qmXXc5aAP+jw+6ft4pO99T9/7mxHy/ZHyD9LwP4hV9aFfadTeVRv/NspXrfyNsW+b0RinUb4q9aPvfW2jfNX6tVbvX7W+LStfAGpLWSgN/RzgXMps3JyBdYsoFX13ynnS5ww87/uUjuJllCu6fw54uNs2nzLrewKls9iFcmX6L/cta7f9jd36+ZRzEG/fzO/tXpRv72ZR7hD1GLBwYPuvKRe+mwMcTZ27XLXKuk23j6spF++bA8zqW9Yu5w2UCZ3ZPahf/yvr67p123a5P0TpBN/Uw6zzKPV/arkWuIAZ3NVjDFl3Bw6hXDtpDvBp4FFgQd+yDjzmBMq3sNHzMtuqT7iE0h5uN7T+FZTDfY/pcn+FF1488FTgD5Q7MexKGXRM3Y2hVf2qnrVlOejDMkJZbD4+aZGt215lPDJCvrH0PY3yVetvWuTrtl9BOTViHuUUk/uZwYVBW+XrHjNye0Kj9o5K498W+SqXvxb5qo1xGn6+I9ePVtn6XDeoOMZq+NnW6dtm+sZv6Qvl9mQ5tJwJbE+5svU6YBWl05g18LwFlHPrHukK0a8YuNMB8A7gzu7DXwV8B5jb06zXdDlXUxrgnTbze3sc8DfKFcvvBY4c2u8elE56PWVGe+Qr1jfMetk0+z2lb1kpt/rMbtvageXQHmbdl3IxMWxzeQAAA5FJREFUyjVdeb4TOLqvZWCa8lDjtuUt3teFA899HLgFOLCPWQceswz40qgZx9QWVO0TKNcASMpddgbr7OJu+zspg8H1lPZyj4HnBuUuJE90y3l0AzQa1K9WWVuWgz4sI5TF5uOThtmqjEdGyDeWvqdFvmle4zJmftvyJvko/8ws6d7DFZRr3GzyP4ct3z9GbE9o2N5RYfzbMl+N8tcqH5XGOI0/35HqR+vPln7XjRr9Wst8Vfq2qcIsSZIkSZKkCeE1dCRJkiRJkiaMEzqSJEmSJEkTxgkdSZIkSZKkCeOEjiRJkiRJ0oRxQkeSJEmSJGnCOKEjSZIkSZI0YZzQkSRJkiRJmjBO6EiaeBFxZUTcGxEvHlp/eERsiIi3ba5skiRp6+UYRVJLTuhI2hKcBiwAvji1IiLmA98Dzs/MO1q86PDgTJIkaYhjFEnNOKEjaeJl5pPAh4HPRMTB3eoLgH8AZwJExKKIWBoRayLikYi4KiJ2ntpHRLw5In4eEY9FxOqI+OXAvoiI2RGREXFqRFwfEeuAs8f2R0qSpInjGEVSS07oSNoiZObNwMXAFRFxLLAYODEzn4mIVwG3AfcABwFHANsDP46I6HYxD7gcOBR4C/A7YGlEvHzopc4Crgf2Ay5p+1dJkqRJ5xhFUiuRmZs7gyRVERHbUgZEewNnZOb53fpzgAMy88iBx+4IPNqtv3uafQXwCPCJzFwSEbOBDcCFmfmp9n+NJEnaUjhGkdSCR+hI2mJk5nrgq8DTwNcGNh0AHBYRa6cW4MFu214AEbFzRHw7Ih6IiNXAGso577sPvcxdLf8GSZK05XGMIqmF2Zs7gCRV9izwXGY+N7DuRcANwGenefyq7ueVlEOcTwceogy4bgWGLyq4rmZYSZK01XCMIqkqJ3QkbQ3uBo4CHszMZ//LY94OfDQzbwKIiFcCu4wpnyRJ2jo5RpE0Y55yJWlrcBGwI3BNRBwcEXtGxBER8d3unHaAB4ATI2Lf7s4RSyjfgEmSJLXiGEXSjDmhI2mLl5krgUOAWcAyYDnwDeApykUEAU6hHM58D3A18C1gxbizSpKkrYdjFEmj8C5XkiRJkiRJE8YjdCRJkiRJkiaMEzqSJEmSJEkTxgkdSZIkSZKkCeOEjiRJkiRJ0oRxQkeSJEmSJGnCOKEjSZIkSZI0YZzQkSRJkiRJmjBO6EiSJEmSJE0YJ3QkSZIkSZImzL8BwMJwqj8bwogAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How the number of the shortest destination flights evolved through the years\n",
    "nf_sfo_oak = df_filtered[(df_filtered.Distance == 11.) & ((df_filtered.Origin=='SFO') | (df_filtered.Origin == 'OAK'))]['Year'].value_counts()\n",
    "nf_jfk_lga = df_filtered[(df_filtered.Distance == 11.) & ((df_filtered.Origin=='JFK') | (df_filtered.Origin == 'LGA'))]['Year'].value_counts()\n",
    "\n",
    "# plot it\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)\n",
    "fig.set_size_inches(16, 4)\n",
    "\n",
    "sns.barplot(nf_sfo_oak.index, nf_sfo_oak.values, ax=ax1)\n",
    "sns.barplot(nf_jfk_lga.index, nf_jfk_lga.values, ax=ax2)\n",
    "\n",
    "ax1.set_title('Number of flights between San Francisco and Oakland airpors')\n",
    "ax2.set_title('Number of flights between John F. Kennedy and LaGuardia airpors')\n",
    "ax1.set_ylabel('Number of flights')\n",
    "ax1.set_xlabel('Year')\n",
    "ax2.set_xlabel('Year')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Oakland, it looks like a regular connection existed for 5 years! (at least).\n",
    "\n",
    "In NYC, just a handful of flights scheduled over the years, probably private, or special services."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Long distance relationship\n",
    "\n",
    "Let's find the most distant airports that have a regular connection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:48:14.752248Z",
     "start_time": "2019-10-14T20:48:14.147346Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################]:  100.00% elapsed time  :        0s =  0.0m =  0.0h \n",
      " "
     ]
    },
    {
     "data": {
      "text/plain": [
       "4962    13045\n",
       "4983     4863\n",
       "4963     2070\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there are frequent flights at uber-long distances\n",
    "df_filtered[df_filtered.Distance > 4900].Distance.value_counts(progress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have few common long distance relations. Let's examine them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:48:25.540106Z",
     "start_time": "2019-10-14T20:48:14.756020Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>#                            </th><th>Origin  </th><th>Dest  </th><th style=\"text-align: right;\">  Distance</th><th style=\"text-align: right;\">  count</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td><i style='opacity: 0.6'>0</i></td><td>EWR     </td><td>HNL   </td><td style=\"text-align: right;\">      4962</td><td style=\"text-align: right;\">   6517</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1</i></td><td>EWR     </td><td>HNL   </td><td style=\"text-align: right;\">      4963</td><td style=\"text-align: right;\">   1035</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>2</i></td><td>HNL     </td><td>EWR   </td><td style=\"text-align: right;\">      4962</td><td style=\"text-align: right;\">   6528</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>3</i></td><td>HNL     </td><td>EWR   </td><td style=\"text-align: right;\">      4963</td><td style=\"text-align: right;\">   1035</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>4</i></td><td>HNL     </td><td>JFK   </td><td style=\"text-align: right;\">      4983</td><td style=\"text-align: right;\">   2432</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>5</i></td><td>JFK     </td><td>HNL   </td><td style=\"text-align: right;\">      4983</td><td style=\"text-align: right;\">   2431</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "  #  Origin    Dest      Distance    count\n",
       "  0  EWR       HNL           4962     6517\n",
       "  1  EWR       HNL           4963     1035\n",
       "  2  HNL       EWR           4962     6528\n",
       "  3  HNL       EWR           4963     1035\n",
       "  4  HNL       JFK           4983     2432\n",
       "  5  JFK       HNL           4983     2431"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by Origin, Destination and Distance, and count the number of flights\n",
    "df_filtered[df_filtered.Distance > 4900].groupby(by=['Origin', 'Dest', 'Distance'], agg={'Origin':'count'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are assentially two pairs of airports. Newark (EWR) --- Honolulu (HNL); and John F. Kennedy (JFK) --- Honolulu (HNL)\n",
    "\n",
    "Notice that between EWR and HNL there are two sets of distances, which are very close to each other. We will examine this in turn. \n",
    "\n",
    "Now let's look at the number of flights per year between these airport pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:48:26.608357Z",
     "start_time": "2019-10-14T20:48:25.541953Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PVfWFVv4vbf4vJFlDd4P7PlO0O7GsbbjtrMvg+HTLvYzum8vX0h3ML6PbX9a7T6yqn9Pdu7Rtr+xUuv3i39tlJd8Gntyb7Vy6S+Um4v8O3X1M/fdzIt3lIle0+q/MIKaiO/N2GfDJJFtOMumxdJegTrw+MMl0H6K7b+Aquhue/6q1M5v1+i/AM9M9xetfe+UfpLufcCaX4E3nLXTJyPVJ/mYe9oPpttH7gPu39oedLTuSLvm4ALgQ+EYrG8XLgDe2ff0Iun8Ip1VV3wGOoXtQw0/p1vmXR2wTuqRsH7q+8fV062DWquq/6O4t+QTdmavd6PrXUbyfbn/5H+BHdJ+hV0wy7eFt2T+juzfwI1MsdybTjmxY/zZCXzFTb6B7WMZq4DN0D73oW+czMWT+WR2f1nO/mnVf13MV3fa6ki65e0lVrWx1r6A7W/hDuktAP0K370xmpsc6bWQmnmYjaROT7vG/766qXaadWFpgSR5D923vrr1LKyVpSuke/39SVc3FlQKSZ5akTUW63+x5SpLN2+UYr6f7plBaVNrlf68E3muiJEkaJ5MladMRuksyfkZ3Gd536S7XkRaNdr/f9XRPpHv7mMORJG3ivAxPkiRJkobwzJIkSZIkDTHZD/VtFLbbbrvaddddxx2GJEmSpEXqf//3f6+pqmXD6jbqZGnXXXflvPPOG3cYkiRJkhapJJdOVudleJIkSZI0hMmSJEmSJA1hsiRJkiRJQ5gsSZIkSdIQJkuSJEmSNITJkiRJkiQNYbIkSZIkSUOYLEmSJEnSECZLkiRJkjTE5uMOQJL6ln/sSeNt/08/P2ndkz95wAJGcnuf3f8TY21fkqRNjcmSNE/+4wN/OLa2n/mCz01Zf9yHxpuQvPh5kyckmp2nnHrkWNs//emvm7L+j045doEiub3PPOOlU9Y/9T8+vECR3N6nn/mcsbW9MfuzUy4aW9snP+M+Y2tb2pBc/c4vjK3t7V/+ByNPa7Kkaa181/5ja3v3//PJsbUtSZI0nZ8cfcXY2v6tw3YaW9ubCpMlSZIWqT/5j9PG1vannvnHY2tbkhYLkyVJkiQtat9879Vja/vBB28/trY1fj4NT5IkSZKGMFmSJEmSpCEWLFlKsnbg9esk7+jV75tkZZIbk5yVZJdeXZIcleTa9jo6SRYqdkmSJEmbngW7Z6mqlkwMJ9ka+Cnw8Ta+HXAKcDBwGvAm4GTgEW2WQ4CnAXsBBZwB/BB49wKFL0mSep7+iS+Nre1TD3jU2NqWtGkZ12V4zwSuBr7Yxp8BrKiqj1fVzcByYK8ku7f6A4FjquryqroCOAY4aGFDliRJkrQpGdfT8A4ETqyqauN7AOdPVFbVDUkubuUrB+vb8B7DFpzkELozUey8885zH7kWlbPf80dja/txL/rM2NqWJEnS/FvwM0tJdgYeC3ywV7wEWD0w6Wpgm0nqVwNLht23VFXHV9XeVbX3smXL5i5wSZIkSZuUcVyG93zgS1X1o17ZWmDpwHRLgTWT1C8F1vbOTEmSJEnSnBpXsvTBgbIVdA9vAG59AMRurfx29W14BZIkSZI0TxY0WUry+8BOtKfg9ZwK7JnkgCRbAkcAF1TVylZ/IvDqJDsl2RE4FDhhgcKWJEmStAla6DNLBwKnVNWafmFVrQIOAN4M/AzYB3h2b5Lj6B4pfiHwbeAzrUySJEmS5sWCPg2vql48Rd2ZwO6T1BVwWHtJkiRJ0rwb1+8sSZIkSdKiZrIkSZIkSUOM60dpJUmSJM2jn779f8fW9g6veujY2p5LnlmSJEmSpCFMliRJkiRpCJMlSZIkSRrCZEmSJEmShjBZkiRJkqQhTJYkSZIkaQiTJUmSJEkawmRJkiRJkoYwWZIkSZKkIUyWJEmSJGkIkyVJkiRJGsJkSZIkSZKGMFmSJEmSpCFMliRJkiRpCJMlSZIkSRpiwZOlJM9O8t0kNyS5OMmjW/m+SVYmuTHJWUl26c2TJEcluba9jk6ShY5dkiRJ0qZjQZOlJE8EjgJeAGwDPAb4YZLtgFOAw4FtgfOAk3uzHgI8DdgLeCDwVODFCxe5JEmSpE3NQp9ZegPwxqr6SlX9pqquqKorgGcAK6rq41V1M7Ac2CvJ7m2+A4FjquryNv0xwEELHLskSZKkTciCJUtJNgP2BpYluSjJ5UnemWQrYA/g/Ilpq+oG4OJWzmB9G96DIZIckuS8JOetWrVqPt6KJEmSpE3ASMlSkscm2ac3flCSLyU5LsmSEdvaAdgCeCbwaOBBwIOB1wFLgNUD06+mu1SPIfWrgSXD7luqquOrau+q2nvZsmUjhiZJkiRJ6xr1zNLbgXsAJLkvcBxwAfB7wFtHXMZN7e87quonVXUN8DbgKcBaYOnA9EuBNW14sH4psLaqasS2JUmSJGlGRk2WdgMubMMHAGdU1cuAFwF/PMoCqupnwOXAsARnBd3DGwBIsnVrc8Ww+ja8AkmSJEmaJ6MmSwVs1ob3BT7Xhq8C7j6D9j4AvCLJ9knuBrwK+DRwKrBnkgOSbAkcAVxQVSvbfCcCr06yU5IdgUOBE2bQriRJkiTNyOYjTvd14PAkZ9Ddb3RIK9+VLmEa1ZuA7YDvAzcDHwPeXFU3JzkAeCdwEvBV4Nm9+Y4D7s1tZ7fe28okSZIkaV6Mmiz9NfBhYH+65ObiVv4s4NxRG6uqXwEva6/BujOB3W83U1dXwGHtJUmSJEnzbqRkqaoupPsx2EF/A9wypxFJkiRJ0iIw6qPD/zvJXYdU3RH4wtyGJEmSJEnjN+oDHh5HlxgN2pLuHiZJkiRJ2qhMeRlekof0Rh+Y5Lre+GbAk4Ar5iMwSZIkSRqn6e5ZOo/useHF8MvtbgJeMddBSZIkSdK4TZcs/TYQ4IfAw4FVvbpfAldX1a/nKTZJkiRJGpspk6WqurQNjnpvkyRJkiRtFEb9nSWS3IvuYQ7bM5A8VdXb5jguSZIkLaDPnnzN2Np+8p9tN7a2pamMlCwleQ7wfrrfVFpFdw/ThAJMliRJkiRtVEY9s/RG4BjgcO9RkiRJkrQpGPVepB2A95ooSZIkSdpUjJosnQ7sM5+BSJIkSdJiMulleEme0Rs9AzgqyR7AhcCv+tNW1SnzE54kSZIkjcdU9yz9x5Cy1w4pK2CzuQlHkiRJkhaHSZOlqvK3lSRJkiRtskyIJEmSJGmIUX9n6YhJqgq4GbgI+FxV3TRXgUmSJEnSOI36O0vPAnYGtgaubGU7AjfQ/UjtvYCrkzy2qn4451FKkiRJ0gIb9TK8Y4CvA7tW1c5VtTOwK/BVuh+s3RH4PvDPUy0kydlJbk6ytr2+16vbN8nKJDcmOSvJLr26JDkqybXtdXSSzOytSpIkSdLoRk2WXg+8uqounyhow4cBb6yqa4F/AB4xwrJeXlVL2uu+AEm2A04BDge2Bc4DTu7NcwjwNGAv4IHAU4EXjxi7JEmSJM3YqMnSDsCWQ8rvBGzfhn8K3HmWcTwDWFFVH6+qm4HlwF5Jdm/1BwLHVNXlVXUF3Zmug2bZliRJkiRNa9Rk6UzguCQPS3KH9noYcCzdD9YCPAD40QjLekuSa5J8OcnjWtkewPkTE1TVDcDFrfx29W14D4ZIckiS85Kct2rVqhHfniRJkiSta9Rk6WC6M0dfBX7RXl9pZS9q06wB/maa5fwdcG9gJ+B44LQkuwFLgNUD064GtmnDg/WrgSXD7luqquOrau+q2nvZsmWjvTtJkiRJGjDS0/Cq6mrgD5PcF7gvEOC7VfX93jRnjbCcr/ZGP5jkz4GnAGuBpQOTL6VLwBhSvxRYW1U1SvySJEmSNFOjPjocgKr6HvC9aSecwSLpEq8VdPclAZBka2C3Vk77uxfwtTa+V69OkiRJkubcpMlSkn8F/r6qbmjDk6qqv5quoSR3BfYBzgFuAf4MeAzwKuA64K1JDgA+AxwBXFBVK9vsJwKvTnI6XYJ1KPCO6dqUJEmSpNma6szSA4AtesOTGfVSuC2AI4HdgV8DK4GntbNVtETpncBJdPdGPbs373F09zpd2Mbf28okSZIkaV5MmixV1eOHDc9WVa0CHjZF/Zl0idSwuqL7TafD1jcOSZIkSRrFqE/DkyRJkqRNynT3LI1klHuWJEmSJGlDMt09S6Pw8d2SJEmSNjpTJUsHApdX1W8WKhhJkiRJWiymumfpR8DdAZL8d3v0tyRJkiRtEqZKltYAy9rw47jtMeKSJEmStNGb6jK8M4H/TvLdNn5qkl8Om7CqnjDnkUmSJEnSGE2VLD0PeCFwH+CxwPeAGxciKEmSJEkat6l+lPYm4F0ASR4EHFpV1y9UYJIkSZI0TlOdWbpVVT1+vgORJEmSpMVkqgc8SJIkSdImy2RJkiRJkoYwWZIkSZKkISZNlpK8P8k2bfgxSUa6v0mSJEmSNgZTnVl6LrB1Gz4L2Hb+w5EkSZKkxWGqs0WXAK9I8gUgwO8l+dmwCavqf+YhNkmSJEkam6mSpb8F3gP8PVDAqZNMV8BmcxyXJEmSJI3VVD9K+0ngk0nuClwH7AFcvVCBSZIkSdI4Tfs0vKq6Hng88IOqunbYa6aNJvmdJDcnOalXtm+SlUluTHJWkl16dUlyVJJr2+voJJlpu5IkSZI0qpGecFdV5yS5U5LnA/enu/TuO8BHquoXs2j3XcDXJ0aSbAecAhwMnAa8CTgZeESb5BDgacBere0zgB8C755F24vSVcceOba27/HS142tbUmSJGmxGul3lpLcH/g+8DZgH7ok5p+B7ye530waTPJs4Hrgv3rFzwBWVNXHq+pmYDmwV5LdW/2BwDFVdXlVXQEcAxw0k3YlSZIkaSZG/VHafwG+BexcVY+uqkcDOwPnA28ftbEkS4E3AocOVO3RlgVAVd0AXNzKb1ffhvdgiCSHJDkvyXmrVq0aNTRJkiRJWseoydIjgddW1c8nCtrwPwCPmkF7bwLeV1WXDZQvAVYPlK0GtpmkfjWwZNh9S1V1fFXtXVV7L1u2bAahSZIkSdJtRrpnCbgZuOuQ8ru0umkleRCwH/DgIdVrgaUDZUuBNZPULwXWVlWN0rYkSZIkzdSoZ5ZOA96T5JFJNmuvRwHHAZ8acRmPA3YFfpzkKuBvgAOSfANYQffwBgCSbA3s1soZrG/DK5AkSZKkeTJqsvRK4AfAF+nOJN0MnEP30IdXjbiM4+kSoAe117uBzwBPovvB2z2THJBkS+AI4IKqWtnmPRF4dZKdkuxId8/TCSO2K0mSJEkzNuqjw68H9k9yH+B+QIDvVNVFozZUVTcCN06MJ1kL3FxVq9r4AcA7gZOArwLP7s1+HHBv4MI2/t5WJkmSJEnzYtR7lgBoydHICdI0y1o+MH4msPsk0xZwWHtJkiRJ0rwb9TI8SZIkSdqkmCxJkiRJ0hAmS5IkSZI0xLTJUpLNk7ysPYVOkiRJkjYJ0z7goapuSfJWusd8S5IkaZbedepPx9b2/3n6DmNrW9pQjXoZ3leAh8xnIJIkSZK0mIz66PD3AMck2QX4X+CGfmVVfWOuA5MkSZKkcRo1WfpI+/u2IXUFbDY34UiSJEnS4jBqsvTb8xqFJEmSJC0yIyVLVXXpfAciSZIkSYvJyL+zlOTJST6d5DtJ7tXKDk6y7/yFJ0mSJEnjMVKylOQ5wMeAH9BdkrdFq9oMOGx+QpMkSZKk8Rn1zNJhwIuq6q+BW3rlXwEeNOdRSZIkSdKYjZos/Q7w/4aUrwWWzl04kiRJkrQ4jJosXQn87pDyxwAXz104kiRJkrQ4jJosHQ/8a5JHtvF7JTkQOBo4dl4ikyRJkqQxGvXR4UcnuQtwBrAlcBbwC+Cfqupd8xifJEmSJI3FqD9KS1X9Q5I3A/enOyP1napaO2+RSZIkSdIYjfw7S00BNwM3Ar+eaWNJTkrykyQ/T/L9JAf36vZNsjLJjUnOSrJLry5JjkpybXsdnSQzbV+SJEmSRjXq7yzdKcnbgeuA84ELgOuS/EuSLWfQ3luAXatqKfAnwJFJHppkO+AU4HBgW+A84OTefIcATwP2Ah4IPBV48QzalSRJkqQZGfXM0rHAM4GD6R4jfp82/HTg30ZtrKpWVNUvJkbbazfgGcCKqvp4Vd0MLAf2SrJ7m/ZA4JiquryqrrsWvj8AABVzSURBVACOAQ4atV1JkiRJmqlRk6VnAS+oqg9X1Q/b68PAX9IlUSNL8m9JbgRWAj8BTgf2oDtjBUBV3UD3SPI9WtE69W14D4ZIckiS85Kct2rVqpmEJkmSJEm3GjVZugG4Ykj5FcBNM2mwql4GbAM8mu7Su18AS4DVA5OubtMxpH41sGTYfUtVdXxV7V1Vey9btmwmoUmSJEnSrUZNlt4BvD7JVhMFbfjwVjcjVfXrqvoScE/gpcBaYOnAZEuBNW14sH4psLaqaqZtS5IkSdIoJn10eJJPDRQ9DrgiyQVt/AFt/q3Xs/3dgBV09yVNtL11r5z2dy/ga218r16dJEmSJM25qX5n6dqB8U8MjP9oJg0l2R54AvBpukv39gP+HPgL4FzgrUkOAD4DHAFcUFUr2+wnAq9OcjrdQyEOZRZntCRJkiRpVJMmS1X1gjluq+guuXs33eV/lwKvqqpPArRE6Z3AScBXgWf35j0OuDdwYRt/byuTJEmSpHkx1ZmlOVVVq4DHTlF/JrD7JHUFHNZekiRJkjTvRkqWktyN7rePHg9sz8CDIapq+zmPTJIkSZLGaNQzSyfS/a7RB4Gf0l1SJ0mSJEkbrVGTpccBj62qb8xjLJIkSZK0aIz6O0sXz2BaSZIkSdrgjZoAvRJ4S5K9kmw2nwFJkiRJ0mIw6mV4FwFbAd8ASLJOZVWZQEmSJEnaqIyaLH0UuAvwV/iAB0mSJEmbgFGTpb2Bh1fVt+czGEmSJElaLEa9Z+k7wNL5DESSJEmSFpNRk6XXAW9Lsl+SHZJs23/NZ4CSJEmSNA6jXoZ3evv7Bda9Xylt3Ac8SJIkSdqojJosPX5eo5AkSZKkRWakZKmqzpnvQCRJkiRpMRkpWUrykKnqq+obcxOOJEmSJC0Oo16Gdx7dvUn9X6Pt37vkPUuSJEmSNiqjJku/PTC+BfBg4B+Av5/TiCRJkiRpERj1nqVLhxRflGQ18Hrgs3MalSRJkiSN2ai/szSZHwEPmotAJEmSJGkxGSlZGvwR2iR3T7In8BbgeyMu405J3pfk0iRrknwzyZN79fsmWZnkxiRnJdmlV5ckRyW5tr2OTpLhLUmSJEnS+hv1nqVrWPeBDtA97OEy4M9m0NZlwGOBHwNPAT6W5AHAWuAU4GDgNOBNwMnAI9q8hwBPA/ZqcZwB/BB494htS5IkSdKMzPZHaX8DrAIuqqpbRllAVd0ALO8VfTrJj4CHAncHVlTVxwGSLAeuSbJ7Va0EDgSOqarLW/0xwIswWZIkSZI0T8b2o7RJdgB+F1gBvBQ4v9feDUkuBvYAVra/5/dmP7+VSZIkSdK8mDJZSrLtKAupqutm0miSLYAPAx+sqpVJltCdqepbDWzThpe08X7dkiSpqnUuD0xyCN1le+y8884zCUuSJEmSbjXdmaVh9yoNqhGWc6skdwA+BPwSeHkrXgssHZh0KbBmkvqlwNrBRAmgqo4HjgfYe++9p4tdkiRJkoaaLskZvFep7w+BVwIj3bME3VPtgPcBOwBPqapftaoVdPclTUy3NbBbK5+o3wv4Whvfq1cnSZIkSXNuymRp2L1KSR4CHAU8BjiO7sl1ozoWuB+wX1Xd1Cs/FXhrkgOAzwBHABe0hzsAnAi8OsnpdGeyDgXeMYN2JUmSJGlGRv5R2iS/neQjwFeB64D7V9VfVdXgvUaTzb8L8GK6H7G9Ksna9npOW8YBwJuBnwH7AM/uzX4c3SPFLwS+TZdQHTdq7JIkSZI0U9Pea5Tk7nRnel4CfBn4vao6b6YNVdWldL/NNFn9mcDuk9QVcFh7SZIkSdK8m/LMUpLXAhfT/ZDs/lX1hNkkSpIkSZK0oZnuzNKRwE3A5cDLkrxs2ERV9SdzHZgkSZIkjdN0ydKJTP/ocEmSJEna6Ez3NLyDFigOSZIkSVpURv4x2Y3BqmNPGlvby1763LG1LUmSJGnmRn50uCRJkiRtSkyWJEmSJGkIkyVJkiRJGsJkSZIkSZKGMFmSJEmSpCFMliRJkiRpCJMlSZIkSRrCZEmSJEmShjBZkiRJkqQhTJYkSZIkaQiTJUmSJEkawmRJkiRJkoYwWZIkSZKkIUyWJEmSJGmIBU2Wkrw8yXlJfpHkhIG6fZOsTHJjkrOS7NKrS5KjklzbXkcnyULGLkmSJGnTstBnlq4EjgTe3y9Msh1wCnA4sC1wHnByb5JDgKcBewEPBJ4KvHgB4pUkSZK0iVrQZKmqTqmq/wSuHah6BrCiqj5eVTcDy4G9kuze6g8Ejqmqy6vqCuAY4KAFCluSJEnSJmix3LO0B3D+xEhV3QBc3MpvV9+G92CIJIe0S/3OW7Vq1TyFK0mSJGljt1iSpSXA6oGy1cA2k9SvBpYMu2+pqo6vqr2rau9ly5bNS7CSJEmSNn6LJVlaCywdKFsKrJmkfimwtqpqAWKTJEmStAlaLMnSCrqHNwCQZGtgt1Z+u/o2vAJJkiRJmicL/ejwzZNsCWwGbJZkyySbA6cCeyY5oNUfAVxQVSvbrCcCr06yU5IdgUOBExYydkmSJEmbloU+s/Q64CbgNcBz2/DrqmoVcADwZuBnwD7As3vzHQecBlwIfBv4TCuTJEmSpHmx+UI2VlXL6R4LPqzuTGD3SeoKOKy9JEmSJGneLZZ7liRJkiRpUTFZkiRJkqQhTJYkSZIkaQiTJUmSJEkawmRJkiRJkoYwWZIkSZKkIUyWJEmSJGkIkyVJkiRJGsJkSZIkSZKGMFmSJEmSpCFMliRJkiRpCJMlSZIkSRrCZEmSJEmShjBZkiRJkqQhTJYkSZIkaQiTJUmSJEkawmRJkiRJkoYwWZIkSZKkITaYZCnJtklOTXJDkkuT/MW4Y5IkSZK08dp83AHMwLuAXwI7AA8CPpPk/KpaMd6wJEmSJG2MNogzS0m2Bg4ADq+qtVX1JeBTwPPGG5kkSZKkjVWqatwxTCvJg4Fzq2qrXtnfAI+tqj8emPYQ4JA2el/ge3MYynbANXO4vLlkbLNjbLNjbLNjbLNjbLNjbLNjbLO3mOMzttnZVGLbpaqWDavYUC7DWwKsHihbDWwzOGFVHQ8cPx9BJDmvqvaej2WvL2ObHWObHWObHWObHWObHWObHWObvcUcn7HNjrFtIJfhAWuBpQNlS4E1Y4hFkiRJ0iZgQ0mWvg9snuR3emV7AT7cQZIkSdK82CCSpaq6ATgFeGOSrZM8Etgf+NAChzIvl/fNEWObHWObHWObHWObHWObHWObHWObvcUcn7HNziYf2wbxgAfofmcJeD/wROBa4DVV9ZHxRiVJkiRpY7XBJEuSJEmStJA2iMvwJEmSJGmhmSxJkiRJ0hCbZLKU5OVJzkvyiyQnDNQdnOSiJGuTfC7Jjr26OyV5d5KfJrkuyWlJdurVPyjJF5OsTnJ5kiMWMLa7Jvlgkqvba/nAvIs5tt9P8rUka5JckORRM4zrTknel+TStoxvJnlyr37fJCuT3JjkrCS79OqS5Kgk17bX0UnSq9+1zXNjW8Z+iyi2NyW5MMktg+t03PEl2T7JR5Nc2fa5LyfZZzHE1urPSrIqyc+TnJ9k/8USW2+6xyapJEcultiSXJLkpnSf87VJvrBYYmvTvDLJj5LckOS7SX533LEl2bm3viZeleTQccfW6tfr2DDPsY3z2PD4VrY6ySVDlj3OY8N0sa3XsWE+Y+tNN47+bbr1Ns7+bdr1lvH1b5PGlvH3b9Nt0/X+3xeAqtrkXsAzgKcBxwIn9MofC1wN7AHcsdWf06s/DDgf2AHYku5pfKf06r8DvBnYDNgN+AnwJwsU2weAjwN3BnYFLgZesNhjA7al+/XlZ7XYngv8DLjbDOLaGljeln0H4Kl0v8G1K92vO69uy98SeCvwld68Lwa+B9wT2Kmtp5f06v8f8DZgK+AA4Hpg2SKJ7UDgycAngeWz/CzMS3zAvYFXA7/VtushbTsvGXdsrf6BwOZteJ+23N9aDLG1abYAvgV8BThyMWzTVn8JsN9s9rUFiO1g4ALg/kDo+rltF0NsA+38NvBrYNfFEBvreWyYr9gY/7Hh4cDz6PquS4Yse5zHhuliW69jw3zGNub+bbr1dgnj69+mi22c/du027Q37UL3b9Ott/X+37eqNs1kqbcSj2Tdf/r/CXhXb3xHoIDd2vixwNG9+j8CvtcbvxG4f2/848DfL1Bs1wAP69W/FvjiYo+tfShWDCz/+8Bfrue2vYDuAHYIcG6vfGvgJmD3Nn4ucEiv/i8nPojA7wK/ALbp1X+RSf4JWsjYBpZ3ErNMlhYivl79z4GHLrbY6Drbm4GHL5bYgNcARwMnMMN/JuYzNtbzn4n5io3uAHsZsO9ii23IMl8PnLVYYmMOjw1zvE3Hemzole/HwD9hjPnYMFVsA/VzdmyYy9gYU/82XWyMsX+bZn8ba/826v7WplnQ/m2EbTon/dsmeRneFNJe/XGAPdvf9wGPTLJjkjsDzwE+25v+7cDzk2yR5L7A7wFnLlBsDKnv1y3W2AbnHayfeTDJDnQHsxV0Z7vOn6ir7je7Lm7lDNa34X7dD6tqzST144xtXsxXfEkeRHfW8aLFEluSTye5GfgqcDZw3mKIrV1i8ELgjbONZ75iaz6c7hLGLyTZa5HEds/22jPJZe1SlTckmfUxbh4/q88HPjjbuOYhtjk9NsxhbOM+Nkxl3MeGBTWXsY25fxvFuPq3qYy7f5uJhe7fpjMn/ZvJ0rpOB/40yQOTbAUcQXeG5M6t/vvAj4Er6L4lvx/rfuA/DTyTLutdCbyvqr6+QLF9DnhNkm2S3IeuM7pzb/7FGtu5wI5J/rztzAfSnSq9M7OQZAvgw8AHq2olsITuFG7famCbNjxYvxpYkiQjzDvO2ObcfMWXZCndJatvqKrB5Y0ttqp6apv+KcDnq+o3iyS2fwUOr6q1s4lnnmN7Dt2lEbsAZwGfT3LXRRDbPVvZHwAPAB4P/DndmYpxx9Zf7qPpLuP+j9nENU+xzdmxYY5jG/exYSrjPjYsmHmIbZz923TG2b9NZdz926jLHUf/Np056d9Mlnqq6r/oTiF+AriU7pTsGuDyNsmxdNdM3p3uVOAptDNL6X4093N0ydOWwL2AJyV52QLF9ld0O8MP6K5T/uhE3WKOraquBfanu7/lp8Af0mX9lzND7VuWDwG/BF7eitcCSwcmXdriG1a/FFhb3fna6eYdZ2xzar7ia8nzaXSX1rxlMcUGUFW/qqrP0n0e/mTcsSX5Y7pLe06eaSzzHRtAVX25qm6qqhvb9rweePQiiO2mVnZ0VV1fVZcAx9ElwuOOre9A4BOz/UdxHva3OTs2zHVsi+DYMJVxHxsWxFzHtgj6tymNuX+byrj7t1GNo3+banlz1r+ZLA2oqndV1e9U1fZ0//xvDny7Ve9Fd6/OdVX1C+AdwMOTbEd3Q/uvq+rEqrqlqi4H/p1Z7Myzia3F9JyqukdV7UG3bb/WZl3MsVFV51TVw6pqW7ob9e7brx9F+ybyfXTfahxQVb9qVSvottvEdFvTfTu5Ylh9G+7X3TvJNpPUjzO2OTNf8SW5E/CfdGdiX7yYYhti8zb/uGPbF9g7yVVJrgL+DHhVkk8ugtiGKW5/qdQ4Yvse3QF2vb5ImM/11r44eBazvERlnmKbk2PDfK23MR8bpjLuY8O8m6fYxt2/zdRC9m9TGXf/Nsqyx9W/TWXu/vetObxZbEN50f1jtCXwFrosdste2Z50H46d6e5j+MfefB+gSwTuQvc0l9cCV7S6pXTfQvwFXTJwD7qn5bx5gWLbje6M12Z0T8G5BthjscfW6h/c1udSuutLvzyLbfpuuifrLBkoX0Z3yvaAFudRrHtz80uA79I9iWlHug9g/0lRX6F7gMWWwNOZ4ROP5jm2Ldp8H6F76MaWwGaLYd212E6jS5Y2X4/P6nzEtnvbD7dqcT6X7kD0kEUQ2zZ0n8+J18nAPzODpx7NY2w7A4+ku/dsS+BvgVXA3ccdW6s/ke6Si23oLltZyQwfBjBfsbVp/oLuzHsW0Wdhro4N87VNx3lsuEMrf3LbblsCd+zVj/PYMF1s631smI/YGH//NlVs4+7fptum4+zfpoytTTOu/m2qbTon/VtVbbLJ0nK6DL3/Wg7cle4JHDcAV9ElBZv15rs73bWUV7cN8CV6T9ACngB8vW3Yq4D3AHdeoNj+FLiS7skf3wKeNLDcxRzbR1tcq+k6z+1nGNcuLZab6U7ZTrye0+r3o+tYbqJL5HbtzRu6p/Jc115H0/uw012/fHab93vM8Ek58xzbCUO2x0GLIT66x8lX2+b95T56EcR2P7qHOqyh+xx/HXj6YlhvQ9o5gZk/Wne+1tse3PY5vxb4L2DvxRBbq19K963hGronRx0x2XodxzYFPg+8aSbra4HW23odG+Y5tnEeGx7H7fvXs3v1uzK+Y8N0sZ0wpP6gxRDbQDsnsLD926SxMf7+bbptOs7+bdptyvj6t+nW23r/71tVtx4EJUmSJEk93rMkSZIkSUOYLEmSJEnSECZLkiRJkjSEyZIkSZIkDWGyJEmSJElDmCxJkiRJ0hAmS5IkSZI0hMmSJGmDkuSkJN9KcseB8n2T/CrJ748rNknSxsVkSZK0oXk5cHfg9RMFSZYC7wfeWlXnzkejg8mZJGnjZ7IkSdqgVNX1wAuAw5I8vBX/M/AzYDlAkj2TfDbJmiRXJ/lwkh0mlpFknyRnJLkmyeokX+wtiySbJ6kkL0nyySQ3AG9csDcpSVoUTJYkSRucqjoTOBY4MckzgecAz6uqXybZCTgH+CbwMOCJwF2BU5OkLWIb4IPAo4FHABcCn01yt4Gm3gB8EngA8O75fVeSpMUmVTXuGCRJmrEkW9ElRL8DvKaq3trK/xF4aFU9qTftdsCqVv6NIcsKcDXwiqr69ySbA78C3l5Vfz3/70aStBh5ZkmStEGqqpuAfwJ+ARzTq3oo8PgkaydewCWtbjeAJDskOT7J95OsBtbQ3Qe180Az583ne5AkLW6bjzsASZLWwy3Ab6rqN72yOwCnAX83ZPqr2t+T6C7NexVwKV3CdTYw+BCHG+YyWEnShsVkSZK0sfkGsD9wSVXdMsk0jwIOqarTAZL8FnCPBYpPkrSB8DI8SdLG5h3AdsBHkzw8yb2TPDHJe9t9TgDfB56X5H7tKXj/Tnd2SZKkW5ksSZI2KlV1OfBIYDPg88AK4J3AjXQPbQA4iO4yvG8CHwGOAy5b6FglSYubT8OTJEmSpCE8syRJkiRJQ5gsSZIkSdIQJkuSJEmSNITJkiRJkiQNYbIkSZIkSUOYLEmSJEnSECZLkiRJkjSEyZIkSZIkDfH/AacPRPTgwTgEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Flight between Newark and Honolulu airport (the more common distance of 4962 miles)\n",
    "nf_ewr_nhl = df_filtered[(df_filtered.Distance == 4962)]['Year'].value_counts()\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "sns.barplot(nf_ewr_nhl.index, nf_ewr_nhl.values)\n",
    "plt.title('Number of flights between Newark Liberty International and Honolulu International airpors')\n",
    "plt.ylabel('Number of flights')\n",
    "plt.xlabel('Year')\n",
    "plt.show()\n",
    "\n",
    "# Pretty much 2 per day since 1999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:48:27.838656Z",
     "start_time": "2019-10-14T20:48:26.633959Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Flight between Newark and Honolulu airport (the less common distance of 4963 miles)\n",
    "nf_ewr_nhl = df_filtered[(df_filtered.Distance == 4963)]['Year'].value_counts()\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "sns.barplot(nf_ewr_nhl.index, nf_ewr_nhl.values)\n",
    "plt.title('Number of flights between Newark Liberty International and Honolulu International airpors')\n",
    "plt.ylabel('Number of flights')\n",
    "plt.xlabel('Year')\n",
    "plt.show()\n",
    "\n",
    "# Pretty much 2 per day since 1999"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The flights between Honolulu International Airport and Newark Liberty International should travel exactly 4962 miles. However, due to the tragedy in in 2001, the trajectory of the route has been changed slightly, accounting for the change in the distance flown. If we sum up the above two histograms, we can see that the service was never suspended: \n",
    "2 flights per day since 1999!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:48:29.371721Z",
     "start_time": "2019-10-14T20:48:27.841367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Flight between Newark and Honolulu  airport (all distances)\n",
    "nf_ewr_nhl = df_filtered[(df_filtered.Distance == 4962) | \n",
    "                         (df_filtered.Distance == 4963)]['Year'].value_counts()\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "sns.barplot(nf_ewr_nhl.index, nf_ewr_nhl.values)\n",
    "plt.title('Number of flights between Newark Liberty International and Honolulu International airpors')\n",
    "plt.ylabel('Number of flights')\n",
    "plt.xlabel('Year')\n",
    "plt.show()\n",
    "\n",
    "# Pretty much 2 per day since 1999"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do the same, now of the right JFK --- HNL pair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:48:30.501292Z",
     "start_time": "2019-10-14T20:48:29.373789Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Flights between John F Kennedy and Honolulu Airpot\n",
    "nf_jfk_nhl = df_filtered[(df_filtered.Distance == 4983)]['Year'].value_counts()\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "sns.barplot(nf_jfk_nhl.index, nf_jfk_nhl.values)\n",
    "plt.title('Number of flights between John F. Kennedy International and Honolulu International airpors')\n",
    "plt.ylabel('Number of flights')\n",
    "plt.xlabel('Year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty much 2 flights per day since 2013 as well. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at some aggregates of the data & identifying the central transport hubs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a lot of airports in this dataset. But most of them do no not contribute much to the over all traffic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:48:36.759997Z",
     "start_time": "2019-10-14T20:48:30.503658Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################]:  100.00% elapsed time  :        6s =  0.1m =  0.0h\n",
      " Total number of flights: 183819525\n",
      "Total number of unique airports: 411\n",
      "Percentage of total flights contributed by the 10 most frequent airports: 33.6%\n",
      "Percentage of total flights contributed by the 30 most frequent airports: 64.6%\n",
      "Percentage of total flights contributed by the 50 most frequent airports: 80.1%\n",
      "Percentage of total flights contributed by the 100 most frequent airports: 93.1%\n"
     ]
    }
   ],
   "source": [
    "# Start by checking the number of flights per origin\n",
    "n_flights_per_origin = df_filtered.Origin.value_counts(progress=True)\n",
    "\n",
    "\n",
    "# See how many of the airports \"contribute\" to the total traffic.\n",
    "print('Total number of flights:', n_flights_per_origin.sum())\n",
    "print('Total number of unique airports:', len(n_flights_per_origin))\n",
    "_ = n_flights_per_origin[:10].sum() / n_flights_per_origin.sum() * 100\n",
    "print('Percentage of total flights contributed by the 10 most frequent airports: %.1f%%' % (_))\n",
    "_ = n_flights_per_origin[:30].sum() / n_flights_per_origin.sum() * 100\n",
    "print('Percentage of total flights contributed by the 30 most frequent airports: %.1f%%' % (_))\n",
    "_ = n_flights_per_origin[:50].sum() / n_flights_per_origin.sum() * 100\n",
    "print('Percentage of total flights contributed by the 50 most frequent airports: %.1f%%' % (_))\n",
    "_ = n_flights_per_origin[:100].sum() / n_flights_per_origin.sum() * 100\n",
    "print('Percentage of total flights contributed by the 100 most frequent airports: %.1f%%' % (_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we wanna turn our attention to just the dominant transport hubs, i.e. the more frequently used airports. \n",
    "To do this, let's construct a DataFrame in which we will aggregate some of the flight data that we are interested in, based on the point of Origin of each flight. We will also count the number of flights departing from each Origin. \n",
    "\n",
    "From the this grouped DataFrame, let's filter out airports with less than 200_000 departures. The rationale is the following: for an airport to be considered frequently used, we require for it to have, on average, 20 destinations per day, one flight per day for each of the over 30 years. These criteria are of course arbitrary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:49:01.477513Z",
     "start_time": "2019-10-14T20:48:36.765408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>#                              </th><th>Origin  </th><th>count  </th><th>Distance_mean    </th><th>Distance_std      </th><th>TaxiIn_mean       </th><th>TaxiIn_std        </th><th>TaxiOut_mean      </th><th>TaxiOut_std       </th><th>DepDelay_mean     </th><th>DepDelay_std      </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td><i style='opacity: 0.6'>0</i>  </td><td>EWR     </td><td>3859089</td><td>949.0571505347506</td><td>683.2925150804604 </td><td>6.838859406285781 </td><td>5.2516574875636035</td><td>25.176960048736476</td><td>16.315227934779696</td><td>12.16040468644496 </td><td>36.88187947317769 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1</i>  </td><td>CVG     </td><td>2236016</td><td>609.4698910920137</td><td>462.1946183756504 </td><td>7.084379192896184 </td><td>25.313514489463852</td><td>16.22206424898427 </td><td>14.008528839181672</td><td>7.816483497436057 </td><td>31.602297095284012</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>2</i>  </td><td>IAH     </td><td>4536870</td><td>823.7763056909279</td><td>478.1311208961596 </td><td>6.222613386537248 </td><td>4.511241307472678 </td><td>18.27043037853273 </td><td>10.227049373119087</td><td>8.016037724277812 </td><td>29.73318498885663 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>3</i>  </td><td>CMH     </td><td>1043900</td><td>553.7959335185362</td><td>419.27614762353124</td><td>7.491396621204861 </td><td>7.516344340461818 </td><td>12.457756452366263</td><td>8.72102111843323  </td><td>7.867450008695839 </td><td>33.14990352504729 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>4</i>  </td><td>MSP     </td><td>3991564</td><td>781.2819729309113</td><td>454.3176216823334 </td><td>6.767775443591901 </td><td>5.983795721994184 </td><td>18.494465320732463</td><td>10.732072657149963</td><td>7.25238365298468  </td><td>29.763247664698596</td></tr>\n",
       "<tr><td>...                            </td><td>...     </td><td>...    </td><td>...              </td><td>...               </td><td>...               </td><td>...               </td><td>...               </td><td>...               </td><td>...               </td><td>...               </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>103</i></td><td>ISP     </td><td>217621 </td><td>680.4168439626691</td><td>399.45987051857014</td><td>5.340379240609876 </td><td>15.117993471455739</td><td>9.534550794316752 </td><td>6.063978121339791 </td><td>7.429246477090541 </td><td>27.84409835266481 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>104</i></td><td>LBB     </td><td>236482 </td><td>330.7310577549243</td><td>111.3834323713817 </td><td>5.9124980334345265</td><td>4.882674768768664 </td><td>8.967333306178974 </td><td>5.144326845523153 </td><td>6.31462146038509  </td><td>27.81948038801572 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>105</i></td><td>MDW     </td><td>2036806</td><td>683.0181455671282</td><td>456.4011863833172 </td><td>5.485737155429885 </td><td>5.139088077261766 </td><td>12.095677916191496</td><td>7.944049952532181 </td><td>11.454665250031036</td><td>30.028289049173726</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>106</i></td><td>JAN     </td><td>274483 </td><td>402.2841451018825</td><td>160.00579622018353</td><td>8.076626032355284 </td><td>10.868612839525154</td><td>10.921605833984447</td><td>6.6165198941456795</td><td>7.065939519638525 </td><td>31.954831690328525</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>107</i></td><td>FAT     </td><td>239846 </td><td>419.3971923651009</td><td>338.8946939867262 </td><td>7.950092585296667 </td><td>5.206262971708373 </td><td>11.36442416995143 </td><td>6.085021086488832 </td><td>6.833798912193658 </td><td>35.815952046865924</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "#    Origin    count    Distance_mean      Distance_std        TaxiIn_mean         TaxiIn_std          TaxiOut_mean        TaxiOut_std         DepDelay_mean       DepDelay_std\n",
       "0    EWR       3859089  949.0571505347506  683.2925150804604   6.838859406285781   5.2516574875636035  25.176960048736476  16.315227934779696  12.16040468644496   36.88187947317769\n",
       "1    CVG       2236016  609.4698910920137  462.1946183756504   7.084379192896184   25.313514489463852  16.22206424898427   14.008528839181672  7.816483497436057   31.602297095284012\n",
       "2    IAH       4536870  823.7763056909279  478.1311208961596   6.222613386537248   4.511241307472678   18.27043037853273   10.227049373119087  8.016037724277812   29.73318498885663\n",
       "3    CMH       1043900  553.7959335185362  419.27614762353124  7.491396621204861   7.516344340461818   12.457756452366263  8.72102111843323    7.867450008695839   33.14990352504729\n",
       "4    MSP       3991564  781.2819729309113  454.3176216823334   6.767775443591901   5.983795721994184   18.494465320732463  10.732072657149963  7.25238365298468    29.763247664698596\n",
       "...  ...       ...      ...                ...                 ...                 ...                 ...                 ...                 ...                 ...\n",
       "103  ISP       217621   680.4168439626691  399.45987051857014  5.340379240609876   15.117993471455739  9.534550794316752   6.063978121339791   7.429246477090541   27.84409835266481\n",
       "104  LBB       236482   330.7310577549243  111.3834323713817   5.9124980334345265  4.882674768768664   8.967333306178974   5.144326845523153   6.31462146038509    27.81948038801572\n",
       "105  MDW       2036806  683.0181455671282  456.4011863833172   5.485737155429885   5.139088077261766   12.095677916191496  7.944049952532181   11.454665250031036  30.028289049173726\n",
       "106  JAN       274483   402.2841451018825  160.00579622018353  8.076626032355284   10.868612839525154  10.921605833984447  6.6165198941456795  7.065939519638525   31.954831690328525\n",
       "107  FAT       239846   419.3971923651009  338.8946939867262   7.950092585296667   5.206262971708373   11.36442416995143   6.085021086488832   6.833798912193658   35.815952046865924"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by origin, calculate some aggregate stats\n",
    "df_group_by_origin = df_filtered.groupby(by='Origin', agg={'Year': 'count',\n",
    "                                                           'Distance': ['mean', 'std'],\n",
    "                                                           'TaxiIn': ['mean', 'std'],\n",
    "                                                           'TaxiOut': ['mean', 'std'],\n",
    "                                                           'DepDelay': ['mean', 'std'],\n",
    "                                                          })\n",
    "\n",
    "# Require to have at least 200k flights, to be considered a typical commercial airport \n",
    "# (~20 destinations with one flight per day for at least 30 years)\n",
    "df_group_by_origin = df_group_by_origin[(df_group_by_origin['count'] > 200_000)]\n",
    "df_group_by_origin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the aggregations, we can examine, for instance, which are the most far-reaching airports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:49:01.503137Z",
     "start_time": "2019-10-14T20:49:01.479496Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>#                            </th><th>Origin  </th><th style=\"text-align: right;\">           count</th><th style=\"text-align: right;\">  Distance_mean</th><th style=\"text-align: right;\">  Distance_std</th><th style=\"text-align: right;\">  TaxiIn_mean</th><th style=\"text-align: right;\">  TaxiIn_std</th><th style=\"text-align: right;\">  TaxiOut_mean</th><th style=\"text-align: right;\">  TaxiOut_std</th><th style=\"text-align: right;\">  DepDelay_mean</th><th style=\"text-align: right;\">  DepDelay_std</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td><i style='opacity: 0.6'>0</i></td><td>SJU     </td><td style=\"text-align: right;\">702358          </td><td style=\"text-align: right;\">       1464.27 </td><td style=\"text-align: right;\">       405.821</td><td style=\"text-align: right;\">      8.51326</td><td style=\"text-align: right;\">     6.78075</td><td style=\"text-align: right;\">      14.767  </td><td style=\"text-align: right;\">      6.08924</td><td style=\"text-align: right;\">        8.07646</td><td style=\"text-align: right;\">       38.908 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1</i></td><td>HNL     </td><td style=\"text-align: right;\">     1.01435e+06</td><td style=\"text-align: right;\">       1434.64 </td><td style=\"text-align: right;\">      1461.09 </td><td style=\"text-align: right;\">      6.24755</td><td style=\"text-align: right;\">     4.49869</td><td style=\"text-align: right;\">      14.1278 </td><td style=\"text-align: right;\">      5.32919</td><td style=\"text-align: right;\">        3.01592</td><td style=\"text-align: right;\">       32.826 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>2</i></td><td>JFK     </td><td style=\"text-align: right;\">     2.37464e+06</td><td style=\"text-align: right;\">       1329.25 </td><td style=\"text-align: right;\">       881.625</td><td style=\"text-align: right;\">      6.90835</td><td style=\"text-align: right;\">     9.6304 </td><td style=\"text-align: right;\">      28.4205 </td><td style=\"text-align: right;\">     17.3369 </td><td style=\"text-align: right;\">       11.6521 </td><td style=\"text-align: right;\">       39.1827</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>3</i></td><td>SEA     </td><td style=\"text-align: right;\">     3.10943e+06</td><td style=\"text-align: right;\">       1177.29 </td><td style=\"text-align: right;\">       632    </td><td style=\"text-align: right;\">      6.46298</td><td style=\"text-align: right;\">     4.91571</td><td style=\"text-align: right;\">      15.5762 </td><td style=\"text-align: right;\">      6.61427</td><td style=\"text-align: right;\">        7.46982</td><td style=\"text-align: right;\">       28.2154</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>4</i></td><td>ANC     </td><td style=\"text-align: right;\">569356          </td><td style=\"text-align: right;\">       1171.8  </td><td style=\"text-align: right;\">       796.034</td><td style=\"text-align: right;\">      5.14451</td><td style=\"text-align: right;\">     3.55129</td><td style=\"text-align: right;\">      12.8845 </td><td style=\"text-align: right;\">      6.35645</td><td style=\"text-align: right;\">        5.93125</td><td style=\"text-align: right;\">       30.087 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>5</i></td><td>OGG     </td><td style=\"text-align: right;\">389631          </td><td style=\"text-align: right;\">       1118.23 </td><td style=\"text-align: right;\">      1248.24 </td><td style=\"text-align: right;\">      6.57531</td><td style=\"text-align: right;\">     4.92173</td><td style=\"text-align: right;\">       8.99997</td><td style=\"text-align: right;\">      4.78332</td><td style=\"text-align: right;\">        2.52952</td><td style=\"text-align: right;\">       34.3253</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>6</i></td><td>SFO     </td><td style=\"text-align: right;\">     4.31266e+06</td><td style=\"text-align: right;\">       1104.17 </td><td style=\"text-align: right;\">       870.573</td><td style=\"text-align: right;\">      6.67325</td><td style=\"text-align: right;\">     5.17686</td><td style=\"text-align: right;\">      17.4708 </td><td style=\"text-align: right;\">      7.7682 </td><td style=\"text-align: right;\">       10.6094 </td><td style=\"text-align: right;\">       32.9309</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>7</i></td><td>LAX     </td><td style=\"text-align: right;\">     6.17816e+06</td><td style=\"text-align: right;\">       1080.4  </td><td style=\"text-align: right;\">       861.132</td><td style=\"text-align: right;\">      6.16223</td><td style=\"text-align: right;\">     4.77581</td><td style=\"text-align: right;\">      15.6503 </td><td style=\"text-align: right;\">      7.52632</td><td style=\"text-align: right;\">        8.33092</td><td style=\"text-align: right;\">       28.1817</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>8</i></td><td>MIA     </td><td style=\"text-align: right;\">     2.18468e+06</td><td style=\"text-align: right;\">        998.621</td><td style=\"text-align: right;\">       502.731</td><td style=\"text-align: right;\">      7.6241 </td><td style=\"text-align: right;\">     6.25082</td><td style=\"text-align: right;\">      18.2477 </td><td style=\"text-align: right;\">      9.4347 </td><td style=\"text-align: right;\">       10.0096 </td><td style=\"text-align: right;\">       34.5715</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>9</i></td><td>EWR     </td><td style=\"text-align: right;\">     3.85909e+06</td><td style=\"text-align: right;\">        949.057</td><td style=\"text-align: right;\">       683.293</td><td style=\"text-align: right;\">      6.83886</td><td style=\"text-align: right;\">     5.25166</td><td style=\"text-align: right;\">      25.177  </td><td style=\"text-align: right;\">     16.3152 </td><td style=\"text-align: right;\">       12.1604 </td><td style=\"text-align: right;\">       36.8819</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "  #  Origin               count    Distance_mean    Distance_std    TaxiIn_mean    TaxiIn_std    TaxiOut_mean    TaxiOut_std    DepDelay_mean    DepDelay_std\n",
       "  0  SJU       702358                   1464.27          405.821        8.51326       6.78075        14.767          6.08924          8.07646         38.908\n",
       "  1  HNL            1.01435e+06         1434.64         1461.09         6.24755       4.49869        14.1278         5.32919          3.01592         32.826\n",
       "  2  JFK            2.37464e+06         1329.25          881.625        6.90835       9.6304         28.4205        17.3369          11.6521          39.1827\n",
       "  3  SEA            3.10943e+06         1177.29          632            6.46298       4.91571        15.5762         6.61427          7.46982         28.2154\n",
       "  4  ANC       569356                   1171.8           796.034        5.14451       3.55129        12.8845         6.35645          5.93125         30.087\n",
       "  5  OGG       389631                   1118.23         1248.24         6.57531       4.92173         8.99997        4.78332          2.52952         34.3253\n",
       "  6  SFO            4.31266e+06         1104.17          870.573        6.67325       5.17686        17.4708         7.7682          10.6094          32.9309\n",
       "  7  LAX            6.17816e+06         1080.4           861.132        6.16223       4.77581        15.6503         7.52632          8.33092         28.1817\n",
       "  8  MIA            2.18468e+06          998.621         502.731        7.6241        6.25082        18.2477         9.4347          10.0096          34.5715\n",
       "  9  EWR            3.85909e+06          949.057         683.293        6.83886       5.25166        25.177         16.3152          12.1604          36.8819"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the grouped dataframe by the mean distance, and show the top 10 rows.\n",
    "df_group_by_origin.sort(by='Distance_mean', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The furthest reaching airports are of course those located in remote places like Hawaii, Alaska, or Puerto Rico. In addition to these, the largest central hubs like LA, NY, SF, Miami are also far-reaching.\n",
    "\n",
    "Next, let's find the mean distance of all the mean distances in the grouped DataFrame. This gives a \"natural\" scale between big cities or populare destination for the U.S.; i.e. it gives a sense of the typical separation beteween important destinations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:49:01.517066Z",
     "start_time": "2019-10-14T20:49:01.505191Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "597.9130442788901"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean Distance\n",
    "df_group_by_origin.Distance_mean.mean()\n",
    "# Median distance\n",
    "np.median(df_group_by_origin.Distance_mean.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Taxi times\n",
    "\n",
    "Let's look at the distribution of mean taxi-in and taxi-out times for the frequent airports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:49:01.747436Z",
     "start_time": "2019-10-14T20:49:01.518832Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Taxing\n",
    "plt.figure(figsize=(8, 4))\n",
    "df_group_by_origin.plot1d('TaxiIn_mean', limits=[0, 30], lw=2, shape=64, label='Taxi-in')\n",
    "df_group_by_origin.plot1d('TaxiOut_mean', limits=[0, 30], lw=2, shape=64, label='Taxi-out')\n",
    "plt.xlabel('Taxi [min]')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Taxing out on average takes much longer than Taxing in: \n",
    "# makes sense, you don't wait so much once the plane landf, but can often wait on \"queue\" before taking off"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's point fingers and find the airports with the shortest and longest taxi times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:49:01.800771Z",
     "start_time": "2019-10-14T20:49:01.749478Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shortest Taxi-In:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>#                            </th><th>Origin  </th><th style=\"text-align: right;\">      count</th><th style=\"text-align: right;\">  Distance_mean</th><th style=\"text-align: right;\">  Distance_std</th><th style=\"text-align: right;\">  TaxiIn_mean</th><th style=\"text-align: right;\">  TaxiIn_std</th><th style=\"text-align: right;\">  TaxiOut_mean</th><th style=\"text-align: right;\">  TaxiOut_std</th><th style=\"text-align: right;\">  DepDelay_mean</th><th style=\"text-align: right;\">  DepDelay_std</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td><i style='opacity: 0.6'>0</i></td><td>DAL     </td><td style=\"text-align: right;\">1.49833e+06</td><td style=\"text-align: right;\">         380.81</td><td style=\"text-align: right;\">       270.675</td><td style=\"text-align: right;\">      4.30246</td><td style=\"text-align: right;\">     3.26236</td><td style=\"text-align: right;\">        9.5211</td><td style=\"text-align: right;\">      5.72323</td><td style=\"text-align: right;\">        9.53513</td><td style=\"text-align: right;\">       24.6417</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "  #  Origin          count    Distance_mean    Distance_std    TaxiIn_mean    TaxiIn_std    TaxiOut_mean    TaxiOut_std    DepDelay_mean    DepDelay_std\n",
       "  0  DAL       1.49833e+06           380.81         270.675        4.30246       3.26236          9.5211        5.72323          9.53513         24.6417"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Longest Taxi-In:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>#                            </th><th>Origin  </th><th style=\"text-align: right;\">  count</th><th style=\"text-align: right;\">  Distance_mean</th><th style=\"text-align: right;\">  Distance_std</th><th style=\"text-align: right;\">  TaxiIn_mean</th><th style=\"text-align: right;\">  TaxiIn_std</th><th style=\"text-align: right;\">  TaxiOut_mean</th><th style=\"text-align: right;\">  TaxiOut_std</th><th style=\"text-align: right;\">  DepDelay_mean</th><th style=\"text-align: right;\">  DepDelay_std</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td><i style='opacity: 0.6'>0</i></td><td>ICT     </td><td style=\"text-align: right;\"> 309076</td><td style=\"text-align: right;\">        523.074</td><td style=\"text-align: right;\">       192.711</td><td style=\"text-align: right;\">      9.31726</td><td style=\"text-align: right;\">     9.83219</td><td style=\"text-align: right;\">       12.2629</td><td style=\"text-align: right;\">      8.00531</td><td style=\"text-align: right;\">         6.3479</td><td style=\"text-align: right;\">       34.5787</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "  #  Origin      count    Distance_mean    Distance_std    TaxiIn_mean    TaxiIn_std    TaxiOut_mean    TaxiOut_std    DepDelay_mean    DepDelay_std\n",
       "  0  ICT        309076          523.074         192.711        9.31726       9.83219         12.2629        8.00531           6.3479         34.5787"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shortest Taxi-out:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>#                            </th><th>Origin  </th><th style=\"text-align: right;\">  count</th><th style=\"text-align: right;\">  Distance_mean</th><th style=\"text-align: right;\">  Distance_std</th><th style=\"text-align: right;\">  TaxiIn_mean</th><th style=\"text-align: right;\">  TaxiIn_std</th><th style=\"text-align: right;\">  TaxiOut_mean</th><th style=\"text-align: right;\">  TaxiOut_std</th><th style=\"text-align: right;\">  DepDelay_mean</th><th style=\"text-align: right;\">  DepDelay_std</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td><i style='opacity: 0.6'>0</i></td><td>LBB     </td><td style=\"text-align: right;\"> 236482</td><td style=\"text-align: right;\">        330.731</td><td style=\"text-align: right;\">       111.383</td><td style=\"text-align: right;\">       5.9125</td><td style=\"text-align: right;\">     4.88267</td><td style=\"text-align: right;\">       8.96733</td><td style=\"text-align: right;\">      5.14433</td><td style=\"text-align: right;\">        6.31462</td><td style=\"text-align: right;\">       27.8195</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "  #  Origin      count    Distance_mean    Distance_std    TaxiIn_mean    TaxiIn_std    TaxiOut_mean    TaxiOut_std    DepDelay_mean    DepDelay_std\n",
       "  0  LBB        236482          330.731         111.383         5.9125       4.88267         8.96733        5.14433          6.31462         27.8195"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Longest Taxi-out:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>#                            </th><th>Origin  </th><th style=\"text-align: right;\">      count</th><th style=\"text-align: right;\">  Distance_mean</th><th style=\"text-align: right;\">  Distance_std</th><th style=\"text-align: right;\">  TaxiIn_mean</th><th style=\"text-align: right;\">  TaxiIn_std</th><th style=\"text-align: right;\">  TaxiOut_mean</th><th style=\"text-align: right;\">  TaxiOut_std</th><th style=\"text-align: right;\">  DepDelay_mean</th><th style=\"text-align: right;\">  DepDelay_std</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td><i style='opacity: 0.6'>0</i></td><td>JFK     </td><td style=\"text-align: right;\">2.37464e+06</td><td style=\"text-align: right;\">        1329.25</td><td style=\"text-align: right;\">       881.625</td><td style=\"text-align: right;\">      6.90835</td><td style=\"text-align: right;\">      9.6304</td><td style=\"text-align: right;\">       28.4205</td><td style=\"text-align: right;\">      17.3369</td><td style=\"text-align: right;\">        11.6521</td><td style=\"text-align: right;\">       39.1827</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "  #  Origin          count    Distance_mean    Distance_std    TaxiIn_mean    TaxiIn_std    TaxiOut_mean    TaxiOut_std    DepDelay_mean    DepDelay_std\n",
       "  0  JFK       2.37464e+06          1329.25         881.625        6.90835        9.6304         28.4205        17.3369          11.6521         39.1827"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shortest taxi in\n",
    "print('Shortest Taxi-In:')\n",
    "display(\n",
    "    df_group_by_origin.sort(by='TaxiIn_mean').head(1)\n",
    ")\n",
    "\n",
    "\n",
    "# lonest taxi in\n",
    "print('Longest Taxi-In:')\n",
    "display(\n",
    "    df_group_by_origin.sort(by='TaxiIn_mean', ascending=False).head(1)\n",
    ")\n",
    "\n",
    "\n",
    "# shortest taxi out:\n",
    "print('Shortest Taxi-out:')\n",
    "display(\n",
    "    df_group_by_origin.sort(by='TaxiOut_mean').head(1)\n",
    ")\n",
    "\n",
    "print('Longest Taxi-out:')\n",
    "display(\n",
    "    df_group_by_origin.sort(by='TaxiOut_mean', ascending=False).head(1)\n",
    ")\n",
    "\n",
    "\n",
    "# DAL - Dallas Love Field Airport (Texas)\n",
    "# ICT - Wichita Dwight D. Eisenhower (Kansas)\n",
    "# LBB - Lubbock Preston Smith International Airport (Texas)\n",
    "# JFK - John F. Kennedy International Airport (NYC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis of the frequent origins\n",
    "\n",
    "To create a DataFrame containing only the most frequent origins, as selected above, do an inner join between the main DataFrame and the grouped DataFrame, from which the less frequently used airports are already filtered out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:50:47.797780Z",
     "start_time": "2019-10-14T20:50:24.765807Z"
    }
   },
   "outputs": [],
   "source": [
    "# Now only consider the flights emerging from the top 115 Origins.\n",
    "df_top_origins = df_filtered.join(other=df_group_by_origin, \n",
    "                                  on='Origin', \n",
    "                                  how='inner', \n",
    "                                  rsuffix='_')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create a new virtual column (takes no memory!) to indicate whether a flight has been delayed or not. Note that such a column already exists regarding cancellations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:50:47.806163Z",
     "start_time": "2019-10-14T20:50:47.800052Z"
    }
   },
   "outputs": [],
   "source": [
    "# If a flight is being delayed\n",
    "df_top_origins['Delayed'] = (df_top_origins.DepDelay > 0).astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Time evolution\n",
    "\n",
    "Let's see how some of the flight properties change through time. To do this, let's group them by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:52:05.998164Z",
     "start_time": "2019-10-14T20:51:33.556316Z"
    }
   },
   "outputs": [],
   "source": [
    "# group by year\n",
    "df_group_by_year = df_top_origins.groupby(by='Year', \n",
    "                                          agg={'Year': 'count',\n",
    "                                               'Distance': ['mean', 'std'],\n",
    "                                               'TaxiIn': ['mean', 'std'],\n",
    "                                               'TaxiOut': ['mean', 'std'],\n",
    "                                               'DepDelay': ['mean', 'std', 'sum'],\n",
    "                                               'Cancelled': ['sum'],\n",
    "                                               'Delayed': ['sum'],\n",
    "                                               'Diverted': ['sum']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How have the mean taxi times changed throughout the years. Note that these measurements were not recorded prior to 1995."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:52:06.758973Z",
     "start_time": "2019-10-14T20:52:06.000703Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "sns.barplot(df_group_by_year.Year.values, df_group_by_year.TaxiIn_mean.values)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Duration [min]')\n",
    "plt.title('Mean Taxi-in duration')\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.barplot(df_group_by_year.Year.values, df_group_by_year.TaxiOut_mean.values)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Year')\n",
    "plt.title('Mean Taxi-out duration')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The evolution of the mean distance over time. Gives an indication whether airports are connected with more distanant destinations over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:52:07.475703Z",
     "start_time": "2019-10-14T20:52:06.762194Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "sns.barplot(df_group_by_year.Year.values, df_group_by_year.Distance_mean.values)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Mean Distance [miles]')\n",
    "plt.title('Mean distance traveled by the flights')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting.. the distance increases with time, then it becomes somewhat stable, and it increases again after 2014. \n",
    "Probably new destinations are being added or connected to each other.\n",
    "\n",
    "### Cancellations and Delays\n",
    "\n",
    "Now let's turn to something more interesting.. Let's look at the number of flight delays and calncellations over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:52:08.197566Z",
     "start_time": "2019-10-14T20:52:07.477865Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 4))\n",
    "sns.barplot(df_group_by_year.Year.values, df_group_by_year.Delayed_sum.values)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Delays')\n",
    "plt.title('Number of delays per year')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "sns.barplot(df_group_by_year.Year.values, df_group_by_year.Cancelled_sum.values)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Cancellations')\n",
    "plt.title('Number of cancellations per year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the total number of cancellations per year for each cancellation type, for these most common airports.\n",
    "\n",
    "First let's check if the number of cancellations and cancellation codes match:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:52:16.799661Z",
     "start_time": "2019-10-14T20:52:08.199546Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################]:  100.00% elapsed time  :        8s =  0.1m =  0.0h \n",
      " "
     ]
    },
    {
     "data": {
      "text/plain": [
       "missing    171269977\n",
       "             1091693\n",
       "B             216133\n",
       "A             147315\n",
       "C              75065\n",
       "D                257\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of flights that have cancellation codes:\n",
    "df_top_origins.CancellationCode.value_counts(progress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there are two types of \"missing\" values in the case of Cancellation codes. One is an empty string, another is masked values. Let's look if either show any indication of cancelled flights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:52:21.381473Z",
     "start_time": "2019-10-14T20:52:16.801785Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################]:  100.00% elapsed time  :        4s =  0.1m =  0.0h\n",
      " "
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    1091693\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There are cancellation codes that are empty strings. Let's see what they contain\n",
    "df_top_origins[df_top_origins.CancellationCode==''].Cancelled.value_counts(progress=True)\n",
    "\n",
    "# So in the current dataset, all cancellation codes that are empty strings are flights which were not cancelled."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So in the current dataset, all cancellation codes that are empty strings are flights which were not cancelled.\n",
    "\n",
    "Now let's do the same for the masked values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:52:25.289034Z",
     "start_time": "2019-10-14T20:52:21.384943Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################]:  100.00% elapsed time  :        3s =  0.1m =  0.0h \n",
      " "
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    168722025\n",
       "1      2547952\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for cancelled flights masked values of the cancellation codes\n",
    "df_top_origins[df_top_origins.CancellationCode.ismissing()].Cancelled.value_counts(progress=True)\n",
    "\n",
    "# So there are 2_547_952 flights which were cancelled, for which there is no cancellation code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there are 2_547_952 flights which were cancelled, for which there is no cancellation code. We will interpret this as \"reason unknown\" and will simply ignore these.\n",
    "\n",
    "Next up, let's check the total number of cancelled flights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:52:26.707275Z",
     "start_time": "2019-10-14T20:52:25.293186Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################]:  100.00% elapsed time  :        1s =  0.0m =  0.0h\n",
      " "
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    169813731\n",
       "1      2986709\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the total number of cancelled flights\n",
    "df_top_origins.Cancelled.value_counts(progress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check for consistency: the total number of cancelled flights should be equal to the sum of known cancellation codes (A, B, C, D) + the cancelled flights with unknown cancellation code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:52:26.715116Z",
     "start_time": "2019-10-14T20:52:26.710219Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n"
     ]
    }
   ],
   "source": [
    "# Check for consistency\n",
    "# Sum the value counts from the different cancellation codes + those of cancelled flights with missing codes\n",
    "print(2547952 + 216133 + 147315 + 75065 + 257 - 2986709)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that there are 13 flights that do have a cancellation code, but where not marked as cancelled. \n",
    "Let's check this in another way: count the number of flights which were _not_ cancelled, but have any sort of cancellation code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:52:30.130235Z",
     "start_time": "2019-10-14T20:52:26.717334Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(13)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This means that there are probably (13?) flights that do have a cancellation code which were not cancelled.\n",
    "# let's verify that\n",
    "((df_top_origins.Cancelled == 0) & ((df_top_origins.CancellationCode == 'A') |\n",
    "                                    (df_top_origins.CancellationCode == 'B') |\n",
    "                                    (df_top_origins.CancellationCode == 'C') |\n",
    "                                    (df_top_origins.CancellationCode == 'D'))).sum()\n",
    "# This is such a small discrepancy, we decide to let is slide.\n",
    "# In the following analysis of the cancellation code, we will ignore the unknown reason for the cancelled flights\n",
    "# with unspecified cancellation code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We recover the same number of flights (13) which do have a cancellation code, but were marked as not cancelled. This is such a small discrepancy, we decide to ignore it for the present analysis.\n",
    "\n",
    "Now that we have all this figured out: Let's count the number of cancelled flights for each reason per year. Note that the cancellation code as a field in the dataset was introduced in 2003, so we don't need to consider earlier years for this gruoping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:52:36.642761Z",
     "start_time": "2019-10-14T20:52:30.131936Z"
    }
   },
   "outputs": [],
   "source": [
    "# Now groupby year and a cancellation code\n",
    "# The cancellation code was introduced in the database after 2002, hence the filtering\n",
    "df_year_cancel = df_top_origins[df_top_origins.Year > 2002].groupby(by=['Year', 'CancellationCode'], \n",
    "                                                                    agg={'Cancelled': ['sum']})\n",
    "\n",
    "# Keep only the portion of the DataFrame that contains a proper cancellation code:\n",
    "df_year_cancel = df_year_cancel[~((df_year_cancel.CancellationCode == '') | \n",
    "                                  (df_year_cancel.CancellationCode == 'null') )]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add more meaningful names to the cancellation codes. The designations come from the documentation available at the archive from which this dataset was obtained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:52:36.652266Z",
     "start_time": "2019-10-14T20:52:36.644628Z"
    }
   },
   "outputs": [],
   "source": [
    "# Add more meaningful names to the cancellation codes\n",
    "canc_code_mapper = {'A': 'carrier', \n",
    "                    'B': 'weather', \n",
    "                    'C': 'National Airspace System', \n",
    "                    'D': 'Security'}\n",
    "df_year_cancel['CancellationCode_'] = df_year_cancel.CancellationCode.map(canc_code_mapper, allow_missing=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's make a plot showing the number of cancellations, per season, per year. The `seaborn` library is quite useful for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:52:37.637317Z",
     "start_time": "2019-10-14T20:52:36.654376Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 5))\n",
    "sns.barplot(x='Year',\n",
    "            y='Cancelled_sum', \n",
    "            hue='CancellationCode_', \n",
    "            data=df_year_cancel.to_pandas_df(virtual=True), \n",
    "            palette=sns.color_palette(['C0', 'C1', 'C2', 'C3', 'C6']))\n",
    "            \n",
    "plt.yscale('log')\n",
    "plt.legend(bbox_to_anchor=(0.00, 1.1), loc=2, borderaxespad=0., ncol=4)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('Number of cancellations')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's point fingers again and see at which airports flights experience the most delays and cancellations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:52:51.047159Z",
     "start_time": "2019-10-14T20:52:37.639439Z"
    }
   },
   "outputs": [],
   "source": [
    "# groupby origin to investigate popular cancellations\n",
    "df_group_by_origin_cancel = df_top_origins.groupby(by='Origin').agg({'Delayed': ['count', 'sum'],\n",
    "                                                                     'Cancelled': ['sum']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add a couple of new features: the percentage of flights that are delayed or cancelled, for each Origin. Then plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:52:51.608305Z",
     "start_time": "2019-10-14T20:52:51.049188Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_group_by_origin_cancel['delayed_perc'] = df_group_by_origin_cancel.Delayed_sum/df_group_by_origin_cancel['count'] * 100.\n",
    "df_group_by_origin_cancel['cancelled_perc'] = df_group_by_origin_cancel.Cancelled_sum/df_group_by_origin_cancel['count'] * 100.\n",
    "\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "sns.barplot(df_group_by_origin_cancel.sort(by='delayed_perc', ascending=False)['Origin'].tolist()[:11],\n",
    "            df_group_by_origin_cancel.sort(by='delayed_perc', ascending=False)['delayed_perc'].tolist()[:11])\n",
    "# plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Origin airport code')\n",
    "plt.ylabel('Delayed flights (%)')\n",
    "plt.title('Origin of most commonly delayed flights')\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.barplot(df_group_by_origin_cancel.sort(by='cancelled_perc', ascending=False)['Origin'].tolist()[:11],\n",
    "            df_group_by_origin_cancel.sort(by='cancelled_perc', ascending=False)['cancelled_perc'].tolist()[:11])\n",
    "# plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Origin airport code')\n",
    "plt.ylabel('Cancelled flights (%)')\n",
    "plt.title('Origin of most commonly cancelled flights')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pittsburgh International (PIT), Chicago Midway International (MDW) and Dallas International (DFW) are the top 3 airports with the largest percentage of delayed flights.\n",
    "\n",
    "Westchester County Airport (HPN), LaGuardia Airport (LGA) and Portland International Jetport (PWM) are the top 3 airports with the largest percentage of cancelled flights.\n",
    "\n",
    "Now let's reaaaaly point figures and figure out which Carriers experience the most delays and cancellations, regardless of the point of origin. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:53:04.764650Z",
     "start_time": "2019-10-14T20:52:51.610832Z"
    }
   },
   "outputs": [],
   "source": [
    "# groupby origin to investigate popular cancellations\n",
    "df_group_by_carrier_cancel = df_top_origins.groupby(by='UniqueCarrier', agg={'Delayed': ['count', 'sum'],\n",
    "                                                                             'Cancelled': ['sum']})\n",
    "\n",
    "# Add new features: percentage of flights cancelled or delayed\n",
    "df_group_by_carrier_cancel['delayed_perc'] = df_group_by_carrier_cancel.Delayed_sum/df_group_by_carrier_cancel['count'] * 100.\n",
    "df_group_by_carrier_cancel['cancelled_perc'] = df_group_by_carrier_cancel.Cancelled_sum/df_group_by_carrier_cancel['count'] * 100.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To attach the carrier names we will use tha airline carrier auxiliary table from http://stat-computing.org/dataexpo/2009/supplemental-data.html. Now, this website is rather old, so it is possible tha some airlines have merged or for some reason has obtained new code, or maybe new airlines have emerged. Bottom line is, it may not be complete, but let's try it out here anyway."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:53:04.800103Z",
     "start_time": "2019-10-14T20:53:04.766799Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load the carrier name data\n",
    "aux_df_carrers = vaex.from_csv('./data/aux-carriers.csv', copy_index=False)\n",
    "\n",
    "# Create a mapper\n",
    "mapper_carriers = dict(zip(aux_df_carrers.Code.values, aux_df_carrers.Description.values))\n",
    "\n",
    "# Do the mapping\n",
    "df_group_by_carrier_cancel['CarrierName'] = df_group_by_carrier_cancel.UniqueCarrier.map(mapper=mapper_carriers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:53:04.807617Z",
     "start_time": "2019-10-14T20:53:04.802139Z"
    }
   },
   "outputs": [],
   "source": [
    "# Some of the carriers have very long names, so let's shorten them a bit\n",
    "df_group_by_carrier_cancel['CarrierName_short'] = df_group_by_carrier_cancel.CarrierName.str.slice(start=0, stop=23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:53:05.294823Z",
     "start_time": "2019-10-14T20:53:04.809556Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "\n",
    "plt.subplot(121)\n",
    "sns.barplot(df_group_by_carrier_cancel.sort(by='delayed_perc', ascending=False)['delayed_perc'].tolist()[:11],\n",
    "            df_group_by_carrier_cancel.sort(by='delayed_perc', ascending=False)['CarrierName_short'].tolist()[:11])\n",
    "plt.ylabel('Carrier')\n",
    "plt.xlabel('% of delayed flights')\n",
    "plt.title('Airlines with the most delays')\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.barplot(df_group_by_carrier_cancel.sort(by='cancelled_perc', ascending=False)['cancelled_perc'].tolist()[:11],\n",
    "            df_group_by_carrier_cancel.sort(by='cancelled_perc', ascending=False)['CarrierName_short'].tolist()[:11])\n",
    "plt.ylabel('Carrier')\n",
    "plt.xlabel('% of cancelled flights')\n",
    "plt.title('Airlines with the most cancellations')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enough of pointing fingers, let's give some kudos. Let's determine which airlines have the fewest delays and cancellations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:53:05.749748Z",
     "start_time": "2019-10-14T20:53:05.296807Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "\n",
    "plt.subplot(121)\n",
    "sns.barplot(df_group_by_carrier_cancel.sort(by='delayed_perc', ascending=True)['delayed_perc'].tolist()[:11],\n",
    "            df_group_by_carrier_cancel.sort(by='delayed_perc', ascending=True)['CarrierName_short'].tolist()[:11])\n",
    "plt.ylabel('Carrier')\n",
    "plt.xlabel('% of delayed flights')\n",
    "plt.title('Airlines with the fewest delays')\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.barplot(df_group_by_carrier_cancel.sort(by='cancelled_perc', ascending=True)['cancelled_perc'].tolist()[:11],\n",
    "            df_group_by_carrier_cancel.sort(by='cancelled_perc', ascending=True)['CarrierName_short'].tolist()[:11])\n",
    "plt.ylabel('Carrier')\n",
    "plt.xlabel('% of cancelled flights')\n",
    "plt.title('Airlines with the fewest cancellations')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Safest\" airlies in terms of delays or cancellations are the Hawaian. Makes sense, not much traffic jam happening on the islands, and if flights go outside, they travel a long way to they are probably better cared for."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### More connected than ever\n",
    "\n",
    "Next up, let's see the mean number of unique destinations each airport connects to, and how this number evoluves through time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:53:29.841373Z",
     "start_time": "2019-10-14T20:53:05.752004Z"
    }
   },
   "outputs": [],
   "source": [
    "# Aggregate the number of unique destination for each origin for each year\n",
    "tmp_group = df_top_origins.groupby(by=['Year', 'Origin'], agg={'Year': 'count',\n",
    "                                                               'Dest': [vaex.agg.nunique(df_top_origins.Dest, \n",
    "                                                                                         dropna=True)]})\n",
    "# Calculate the mean number of destinations for all origins per year\n",
    "df_mean_num_dest = tmp_group.groupby('Year').agg({'Dest_nunique': ['mean']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:53:30.294022Z",
     "start_time": "2019-10-14T20:53:29.846400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 4))\n",
    "sns.barplot(x=df_mean_num_dest.Year.values, y=df_mean_num_dest.Dest_nunique_mean.values)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Mean number of destinations')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A surge of new destinations in 2018! The world is getting more connected than ever."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Airplane performance\n",
    "\n",
    "Let's take the auxiliary plane data from http://stat-computing.org/dataexpo/2009/supplemental-data.html and look at the typical performance of the aircraf used. \n",
    "\n",
    "**Warning**: this data is certainly out of date by now. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:53:30.567021Z",
     "start_time": "2019-10-14T20:53:30.299533Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load the data about the airplane models\n",
    "aux_df_planes = vaex.from_csv(filename_or_buffer='./data/aux-plane-data.csv', copy_index=False)\n",
    "\n",
    "# A more convenient display of the model/type of each plane\n",
    "aux_df_planes['model_type'] = aux_df_planes.manufacturer + ' ' + aux_df_planes.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:54:08.667733Z",
     "start_time": "2019-10-14T20:53:30.568928Z"
    }
   },
   "outputs": [],
   "source": [
    "# Join to the auxiliary plance data to the _df_top_origins DataFrame\n",
    "df_top_origins_aux = df_top_origins.join(other=aux_df_planes, \n",
    "                                         left_on='TailNum', \n",
    "                                         right_on='tailnum', \n",
    "                                         how='left',\n",
    "                                         rsuffix='_')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a group based on the model type of the aircrafs used. We are interested in the typical speed and distance travelled by each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:54:49.172570Z",
     "start_time": "2019-10-14T20:54:08.670239Z"
    }
   },
   "outputs": [],
   "source": [
    "# Group by model type\n",
    "df_model_type = df_top_origins_aux.groupby(by='model_type', \n",
    "                                           agg={'Speed': 'mean', 'Distance': 'mean'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's display the fastest and furthest reaching aircraft models (on average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-14T20:54:49.705402Z",
     "start_time": "2019-10-14T20:54:49.176264Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort and plot the top 11 fastest and far reaching aircraft. \n",
    "model_top_speed = df_model_type.sort(by='Speed', ascending=False)[['model_type', 'Speed']]\n",
    "model_top_distance = df_model_type.sort(by='Distance', ascending=False)[['model_type', 'Distance']]\n",
    "\n",
    "plt.figure(figsize=(16, 5))\n",
    "\n",
    "plt.subplot(121)\n",
    "sns.barplot(x=model_top_distance.Distance[:11].tolist(), \n",
    "            y=model_top_distance.model_type[:11].tolist())\n",
    "plt.xlabel('Mean distance [miles]')\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.barplot(x=model_top_speed.Speed[:11].tolist(), \n",
    "            y=model_top_speed.model_type[:11].tolist())\n",
    "plt.xlabel('Mean speed [miles/hour]')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thank you for having a look! :)"
   ]
  }
 ],
 "metadata": {
  "gist": {
   "data": {
    "description": "vaex-airlines/airlines-EDA.ipynb",
    "public": false
   },
   "id": ""
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
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